| { | |
| "paper_id": "D17-1000", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T16:13:26.196319Z" | |
| }, | |
| "title": "Last but not least, many thanks to your hosts, our Local Arrangements Chairs, Dirk Hovy and Anders S\u00f8gaard and their team. Their concern has been increasing the enjoyment of your experience, and to that end they proposed a stunning venue, put together an amazing reception and Social Event, chose your conference bags, issued all the invitation letters for visas, helped create all the signs, etc., etc., etc. Dan Hardt, our Sponsorship chair, working with Anders and Dirk, raised an unusual amount of local sponsorships, all to defray the cost of the Social Event. As always, we are extremely indebted to our generous sponsors. Our platinum sponsors are Google, Amazon, Baidu, Apple, Facebook, Bloomberg and Siteimprove. Gold sponsors include IBM Research, Microsoft, eBay, SAP, Textkernel, Maluuba, Zalando, Recruit Institute of Technology and Deloitte. Silver sponsors are Nuance, Oracle, Sogou, Huawei, Duolingo, CVTE, Unsilo and Wizkids. Snap Inc., Grammarly and Yandex are our Bronze sponsors. Finally, many, many thanks to our Area Chairs, our reviewers, and our authors, whose outstanding research is being showcased here for your delectation. Nyd det mens det varer! Best Regards, Martha Palmer EMNLP 2017 General Chair iii", | |
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| { | |
| "first": "Anietie", | |
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| "last": "Andy", | |
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| { | |
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| { | |
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| { | |
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| { | |
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| { | |
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| { | |
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| { | |
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| { | |
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| { | |
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| }, | |
| { | |
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| { | |
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| { | |
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| { | |
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| { | |
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| { | |
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| { | |
| "first": "Timo", | |
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| "last": "Baumann", | |
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| { | |
| "first": "Daniel", | |
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| }, | |
| { | |
| "first": "Beata", | |
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| { | |
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| "last": "N\u00faria", | |
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| { | |
| "first": "Yonatan", | |
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| "last": "Belinkov", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Dane", | |
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| { | |
| "first": "Eric", | |
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| { | |
| "first": "Kedar", | |
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| "last": "Bellare", | |
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| }, | |
| { | |
| "first": "Islam", | |
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| "last": "Belt- Agy;", | |
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| { | |
| "first": "Anja", | |
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| "last": "Belz", | |
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| }, | |
| { | |
| "first": "Emily", | |
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| "M" | |
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| "last": "Bender", | |
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| "email": "" | |
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| { | |
| "first": "Darina", | |
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| "last": "Benikova", | |
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| "email": "" | |
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| { | |
| "first": "Luciana", | |
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| "last": "Benotti", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Jonathan", | |
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| "last": "Berant", | |
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| "email": "" | |
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| { | |
| "first": "Taylor", | |
| "middle": [], | |
| "last": "Berg- Kirkpatrick", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Nicola", | |
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| "last": "Bertoldi", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Laurent", | |
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| "last": "Besacier", | |
| "suffix": "", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Steven", | |
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| "last": "Bethard", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chandra", | |
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| "last": "Bhagavatula", | |
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| "affiliation": {}, | |
| "email": "" | |
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| { | |
| "first": "Suma", | |
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| "last": "Bhat", | |
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| "email": "" | |
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| { | |
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| "last": "Bhatia", | |
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| { | |
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| "last": "Bingel", | |
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| { | |
| "first": "Alexandra", | |
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| "last": "Birch", | |
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| }, | |
| { | |
| "first": "Arianna", | |
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| "last": "Bisazza", | |
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| { | |
| "first": "Yonatan", | |
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| "last": "Bisk", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Johannes", | |
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| "last": "Bjerva", | |
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| "email": "" | |
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| { | |
| "first": "Anders", | |
| "middle": [], | |
| "last": "Bj\u00f6rkelund", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Alan", | |
| "middle": [ | |
| "W" | |
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| "last": "Black", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Eduardo", | |
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| "last": "Blanco", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Blitzer", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Ivan", | |
| "middle": [], | |
| "last": "Bogatyy", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Bernd", | |
| "middle": [], | |
| "last": "Bohnet", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Gemma", | |
| "middle": [], | |
| "last": "Boleda", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kalina", | |
| "middle": [], | |
| "last": "Bontcheva", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Stefan", | |
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| "last": "Bordag", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Johan", | |
| "middle": [], | |
| "last": "Bos", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Houda", | |
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| "last": "Bouamor", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Guillaume", | |
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| "last": "Bouchard", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Samuel", | |
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| "last": "Bowman", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Kristy", | |
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| "last": "Boyer", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "S", | |
| "middle": [ | |
| "R K" | |
| ], | |
| "last": "Branavan", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chlo\u00e9", | |
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| "last": "Braud", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Fabienne", | |
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| "last": "Braune", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Felipe", | |
| "middle": [], | |
| "last": "Bravo- Marquez", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chris", | |
| "middle": [], | |
| "last": "Brew", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chris", | |
| "middle": [], | |
| "last": "Brockett", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Julian", | |
| "middle": [], | |
| "last": "Brooke", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Caroline", | |
| "middle": [], | |
| "last": "Brun", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Dominique", | |
| "middle": [], | |
| "last": "Brunato", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": ";", | |
| "middle": [], | |
| "last": "William", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Christian", | |
| "middle": [], | |
| "last": "Buck", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Paul", | |
| "middle": [], | |
| "last": "Buitelaar", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Florin", | |
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| "last": "Bulgarov", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
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| "first": ";", | |
| "middle": [], | |
| "last": "Wray", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
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| "first": "Jill", | |
| "middle": [], | |
| "last": "Burstein", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Bill", | |
| "middle": [], | |
| "last": "Byrne", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Donna", | |
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| "last": "Byron", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Elena", | |
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| "last": "Cabrio", | |
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| "affiliation": {}, | |
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| }, | |
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| "last": "Cahill", | |
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| "affiliation": {}, | |
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| }, | |
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| "first": "Nicoletta", | |
| "middle": [], | |
| "last": "Calzolari", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Jose", | |
| "middle": [], | |
| "last": "Camacho-Collados", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Erik", | |
| "middle": [], | |
| "last": "Cam- Bria;", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Nicola", | |
| "middle": [], | |
| "last": "Cancedda", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Marie", | |
| "middle": [], | |
| "last": "Candito", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Hailong", | |
| "middle": [], | |
| "last": "Cao", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Cornelia", | |
| "middle": [], | |
| "last": "Caragea", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Dallas", | |
| "middle": [], | |
| "last": "Card", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Xavier", | |
| "middle": [], | |
| "last": "Carreras", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Francisco", | |
| "middle": [], | |
| "last": "Casacuberta", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tommaso", | |
| "middle": [], | |
| "last": "Caselli", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Taylor", | |
| "middle": [], | |
| "last": "Cassidy", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Vittorio", | |
| "middle": [], | |
| "last": "Castelli", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Asli", | |
| "middle": [], | |
| "last": "Celikyilmaz", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Daniel", | |
| "middle": [], | |
| "last": "Cer", | |
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| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "\u00d6zlem", | |
| "middle": [], | |
| "last": "\u00c7etinoglu", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Mauro", | |
| "middle": [], | |
| "last": "Cettolo", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Nathanael", | |
| "middle": [], | |
| "last": "Chambers", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yee", | |
| "middle": [], | |
| "last": "Seng", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Angel", | |
| "middle": [], | |
| "last": "Chang", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Baobao", | |
| "middle": [], | |
| "last": "Chang", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kai-Wei", | |
| "middle": [], | |
| "last": "Chang", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Snigdha", | |
| "middle": [], | |
| "last": "Chaturvedi", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": ";", | |
| "middle": [], | |
| "last": "Wanxiang", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Che", | |
| "middle": [ | |
| ";" | |
| ], | |
| "last": "Ciprian", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chelba", | |
| "middle": [ | |
| ";" | |
| ], | |
| "last": "Boxing", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chen", | |
| "middle": [ | |
| ";" | |
| ], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Danqi", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Hsin-Hsi", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Lei", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tao", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Wenliang", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yidong", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yun-Nung", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Zhiyuan", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Jianpeng", | |
| "middle": [], | |
| "last": "Cheng", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Colin", | |
| "middle": [], | |
| "last": "Cherry", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Jackie", | |
| "middle": [], | |
| "last": "Chi", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kit", | |
| "middle": [], | |
| "last": "Cheung", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Hai", | |
| "middle": [], | |
| "last": "Leong", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Eunsol", | |
| "middle": [], | |
| "last": "Choi", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yejin", | |
| "middle": [], | |
| "last": "Choi", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Christos", | |
| "middle": [], | |
| "last": "Christodoulopoulos", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Grzegorz", | |
| "middle": [], | |
| "last": "Chrupa\u0142a", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tagyoung", | |
| "middle": [], | |
| "last": "Chung", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Philipp", | |
| "middle": [], | |
| "last": "Cimiano", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kevin", | |
| "middle": [], | |
| "last": "Clark", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Stephen", | |
| "middle": [], | |
| "last": "Clark", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Ann", | |
| "middle": [], | |
| "last": "Clifton", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Maximin", | |
| "middle": [], | |
| "last": "Coavoux", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Anne", | |
| "middle": [], | |
| "last": "Cocos", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Nigel", | |
| "middle": [], | |
| "last": "Collier", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Michael", | |
| "middle": [], | |
| "last": "Collins", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Miriam", | |
| "middle": [], | |
| "last": "Connor", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "John", | |
| "middle": [], | |
| "last": "Conroy", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Li", | |
| "middle": [], | |
| "last": "Dong", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Qing", | |
| "middle": [], | |
| "last": "Dou", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Doug", | |
| "middle": [], | |
| "last": "Downey", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Gabriel", | |
| "middle": [], | |
| "last": "Doyle", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Eduard", | |
| "middle": [], | |
| "last": "Dragut", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Mark", | |
| "middle": [], | |
| "last": "Dras", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Markus", | |
| "middle": [], | |
| "last": "Dreyer", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Lan", | |
| "middle": [], | |
| "last": "Du", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Loic", | |
| "middle": [], | |
| "last": "Dugast", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kevin", | |
| "middle": [], | |
| "last": "Duh", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Greg", | |
| "middle": [], | |
| "last": "Durrett", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Chris", | |
| "middle": [], | |
| "last": "Dyer", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Marc", | |
| "middle": [], | |
| "last": "Dymetman", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Richard", | |
| "middle": [], | |
| "last": "Eckart De Castilho", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Judith", | |
| "middle": [], | |
| "last": "Eckle-Kohler", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Maud", | |
| "middle": [], | |
| "last": "Ehrmann", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Andreas", | |
| "middle": [], | |
| "last": "Eisele", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Jason", | |
| "middle": [], | |
| "last": "Eisner", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Asif", | |
| "middle": [], | |
| "last": "Ekbal", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Michael", | |
| "middle": [], | |
| "last": "Elhadad", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": ";", | |
| "middle": [], | |
| "last": "Messina", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "G\u00fcl\u015fen", | |
| "middle": [], | |
| "last": "Eryigit", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Hugo", | |
| "middle": [ | |
| "Jair" | |
| ], | |
| "last": "Escalante", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": ";", | |
| "middle": [], | |
| "last": "Keelan", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "James", | |
| "middle": [], | |
| "last": "Fan", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Federico", | |
| "middle": [], | |
| "last": "Fancellu", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Hao", | |
| "middle": [], | |
| "last": "Fang", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Stefano", | |
| "middle": [], | |
| "last": "Faralli", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Rich\u00e1rd", | |
| "middle": [], | |
| "last": "Farkas", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": ";", | |
| "middle": [], | |
| "last": "Noura", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Manaal", | |
| "middle": [], | |
| "last": "Faruqui", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": ";", | |
| "middle": [], | |
| "last": "Benoit", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Af- Saneh", | |
| "middle": [], | |
| "last": "Fazly", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Marcello", | |
| "middle": [], | |
| "last": "Federico", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Christian", | |
| "middle": [], | |
| "last": "Federmann", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Geli", | |
| "middle": [], | |
| "last": "Fei", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Anna", | |
| "middle": [], | |
| "last": "Feldman", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yang", | |
| "middle": [], | |
| "last": "Feng", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yansong", | |
| "middle": [], | |
| "last": "Feng", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Raquel", | |
| "middle": [], | |
| "last": "Fernandez", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Daniel", | |
| "middle": [], | |
| "last": "Fern\u00e1ndez-Gonz\u00e1lez", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Olivier", | |
| "middle": [], | |
| "last": "Ferret", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Katja", | |
| "middle": [], | |
| "last": "Filippova", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Andrew", | |
| "middle": [], | |
| "last": "Finch", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Nicholas", | |
| "middle": [], | |
| "last": "Fitzgerald", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Jeffrey", | |
| "middle": [], | |
| "last": "Flanigan", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Lucie", | |
| "middle": [], | |
| "last": "Flekova", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Michael", | |
| "middle": [], | |
| "last": "Flor", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Radu", | |
| "middle": [], | |
| "last": "Florian", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Antske", | |
| "middle": [], | |
| "last": "Fokkens", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Jos\u00e9", | |
| "middle": [ | |
| "A R" | |
| ], | |
| "last": "Fonollosa", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Mikel", | |
| "middle": [ | |
| "L" | |
| ], | |
| "last": "Forcada", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Eric", | |
| "middle": [], | |
| "last": "Fosler-Lussier", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "George", | |
| "middle": [], | |
| "last": "Foster", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Jennifer", | |
| "middle": [], | |
| "last": "Foster", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Stefan", | |
| "middle": [ | |
| "L" | |
| ], | |
| "last": "Frank", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Stella", | |
| "middle": [], | |
| "last": "Frank", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Alexander", | |
| "middle": [], | |
| "last": "Fraser", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Lea", | |
| "middle": [], | |
| "last": "Frermann", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Daniel", | |
| "middle": [], | |
| "last": "Fried", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Mario", | |
| "middle": [], | |
| "last": "Fritz", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Xiao", | |
| "middle": [], | |
| "last": "Fu", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "At- Sushi", | |
| "middle": [], | |
| "last": "Fujii", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Matthias", | |
| "middle": [], | |
| "last": "Gall\u00e9", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Michel", | |
| "middle": [], | |
| "last": "Galley", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Michael", | |
| "middle": [], | |
| "last": "Gamon", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kuzman", | |
| "middle": [], | |
| "last": "Ganchev", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Juri", | |
| "middle": [], | |
| "last": "Ganitkevitch", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "", | |
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| "paper_id": "D17-1000", | |
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| "abstract": [], | |
| "body_text": [ | |
| { | |
| "text": "Thank you so much for joining us in Copenhagen! Welcome to a cosmopolitan city of fantastic restaurants, lovely seascapes, rich history, and lots and lots of cyclists!", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Preface by the General Chair", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We have an exciting program lined up for you, with three Invited talks, fifteen workshops, seven tutorials, nine TACL presentations, 322 reviewed papers presented as both oral talks and posters, and twenty-one demos. I am especially grateful to our Program Chairs, Rebecca Hwa and Sebastian Riedel, who did a fantastic job managing a backbreaking 1,500 paper submissions (1466 reviewed papers). This involved 51 Area chairs and 980 reviewers. We tried some new things this year (never conducive to a smooth process) including a more careful handling of the COIs that result from Area Chair submissions, and the addition of a meta-review step to encourage more thoughtful reviewing. We are soliciting feedback on the meta-review process, from both reviewers and authors. Despite the additional time involvement, many of the Area Chairs embraced this new approach, and would like to repeat it. However, there are clearly a few dissenters, since Rebecca and Sebastian ended up writing around 200 meta-reviews themselves at the last minute! We are also trying to raise the visibility and status of the poster sessions by integrating them as parallel sessions alongside oral talks, with poster session chairs. This is in response to the survey results from EMNLP 2015 that indicated a decided preference for smaller, more frequent poster sessions during the day rather than evening mega-sessions. Finally, Rebecca and Sebastian are bringing you three outstanding invited speakers, Dan Jurafsky, Sharon Goldwater, and Nando de Freitas. No program chairs ever worked harder to bring you a superb set of presentations in an attendee friendly setting.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Preface by the General Chair", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "I am also very grateful to Victoria Fossum and Karl Moritz Hermann, our Workshop Chairs, who put together a terrific slate of fifteen workshops, and paid meticulous attention to ensuring that each workshop could hold exactly the poster sessions, invited talks and special events that it required. Our tutorial chairs, Alexandra Birch and Nathan Schneider, also outdid themselves, providing especially tempting tutorial offerings. Matt Post deserves to be singled out, for being an Advisor to our conscientious and successful Handbook Chair, Joachim Bingel, as well as becoming a welcome last minute addition to our excellent team of Demo Chairs, Lucia Specia and Michael Paul. Thanks are due to our Website Chair, Anders Johannsen, who responded promptly and deftly to all of our requests, and to our Student Volunteer and Student Sponsorship Chairs, Zeljko Agic and Yonatan Bisk, who brought you the helpful and energetic volunteers who keep things running smoothly.", | |
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| "section": "Preface by the General Chair", | |
| "sec_num": null | |
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| { | |
| "text": "Welcome to the 2017 Conference on Empirical Methods in Natural Language Processing! This is an exciting year; we have received a new record-high in the number of submissions: 1,509 papers. After discounting early withdraws, duplicates, and other invalid submissions, we sent out 1,418 submissions (836 long papers, 582 short papers) to be reviewed by the program committee. Ultimately, 216 long papers (25.8% acceptance rate) and 107 short papers (18.4% acceptance rate) have been accepted for presentation, making a total of 323 papers and an overall acceptance rate of 22.8%. This year's technical program consists of three invited talks and 113 oral presentations and 219 poster presentations for the 323 long and short accepted papers as well as nine papers accepted to the Transactions of the Association for Computational Linguistics. To accommodate all the presentations in a compressed timeframe, we opted to have plenary sessions for the invited talks and the winners of the Best Paper Awards, while allotting three parallel oral sessions and thematically related poster sessions for all other presentations. We chose to have concurrent poster and oral sessions for several reasons. First, this is the preferred model of the majority (51.6%) of participants who filled out the EMNLP 2015 post-conference survey. Second, this allows us to spread out the poster presentations across three days in smaller thematically related clusters. Finally, this maximises the number of acceptances for the high quality submissions we received; by having more poster sessions, we are able to maintain the acceptance rates at the previous year's level despite an increase in submissions by 40%. It would not have been possible to properly handle such a large number of submissions without the generous voluntary help from all the members of the program committee, which consists of 980 reviewers overseen by 51 area chairs. We continued last year's experiment of defining twelve relatively broad topic areas and assigning multiple area chairs to facilitate consistent ranking of larger sets of papers. Most technical program decisions, from the selection of papers to the modes of presentation to the choice of outstanding papers, are primarily made in a bottom-up fashion: reviewers assessed and scored papers, made recommendations for oral vs poster decisions, and marked papers suitable for best paper awards; area chairs ensured the quality of assessments, encouraged discussions and assembled opinions into their own recommendations; finally, we construct the technical program, considering the recommendations from the area chairs while taking into account venue constraints and balance across areas. A new experimental feature of this year's EMNLP reviewing process is the \"meta review,\" in which the area chairs briefly summarize the major discussions between the reviewers to give authors a more transparent view of the process.", | |
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| "section": "Preface by the Program Committee Co-Chairs", | |
| "sec_num": null | |
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| "text": "Per EMNLP tradition, awards are given to outstanding papers in three categories: Best Long Paper, Best Short Paper, and Best Resource Paper. The selection process is bottom-up: based on the reviewers and area chairs' recommendations, we nominated four papers for each category; we invited expert members to form a Best Papers committee for each category; each committee reviews the candidates and select the winners. The awarded papers will be presented at a special plenary session on the last day of the conference.", | |
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| "text": "We are extremely grateful that three amazing speakers have agreed to give invited talks at EMNLP. Nando de Freitas (Google Deepmind) will discuss simulated physical environments, and whether language would benefit from the development of such environments, and could contribute toward improving such environments and agents within them. Sharon Goldwater (University of Edinburgh) will describe work on developing unsupervised speech technology for those of the world's 7,000 or so languages not spoken in large rich countries. Dan Jurafsky (Stanford University) will talk about processing the language of policing to automatically measure linguistic aspects of the interaction from discourse factors like conversational structure to social factors like respect.", | |
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| "section": "Preface by the Program Committee Co-Chairs", | |
| "sec_num": null | |
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| { | |
| "text": "The conference would not have been possible without the support of various people inside and outside of the committee. In particular, we would like to thank:", | |
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| "section": "Preface by the Program Committee Co-Chairs", | |
| "sec_num": null | |
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| { | |
| "text": "\u2022 Martha Palmer, whose encouragement and advice as the general chair has been invaluable every step of the way;", | |
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| "section": "Preface by the Program Committee Co-Chairs", | |
| "sec_num": null | |
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| { | |
| "text": "\u2022 Chris Callison-Burch, who has given us excellent advice and support in his capacity as the SIGDAT Secretary;", | |
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| "section": "Preface by the Program Committee Co-Chairs", | |
| "sec_num": null | |
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| { | |
| "text": "\u2022 Priscilla Rasmussen, who always has the right answers;", | |
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| "section": "Preface by the Program Committee Co-Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 Xavier Carreras and Kevin Duh, who generously shared their experiences as the chairs of EMNLP 2016;", | |
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| "section": "Preface by the Program Committee Co-Chairs", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Invited Speaker: Dan Jurafsky, Stanford University \"Does This Vehicle Belong to You\"? Processing the Language of Policing for Improving Police-Community Relations\"", | |
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| "sec_num": null | |
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| { | |
| "text": "Abstract: Police body-cameras have the potential to play an important role in understanding and improving police-community relations. In this talk I describe a series of studies conducted by our large interdisciplinary team at Stanford that use speech and natural language processing on body-camera recordings to model the interactions between police officers and community members in traffic stops.", | |
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| "sec_num": null | |
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| { | |
| "text": "We use text and speech features to automatically measure linguistic aspects of the interaction, from discourse factors like conversational structure to social factors like respect. I describe the differences we find in the language directed toward black versus white community members, and offer suggestions for how these findings can be used to help improve the fraught relations between police officers and the communities they serve. Invited Speaker: Sharon Goldwater, University of Edinburgh Towards more universal language technology: unsupervised learning from speech Abstract: Speech and language processing has advanced enormously in the last decade, with successful applications in machine translation, voice-activated search, and even language-enabled personal assistants. Yet these systems typically still rely on learning from very large quantities of human-annotated data. These resource-intensive methods mean that effective technology is available for only a tiny fraction of the world's 7000 or so languages, mainly those spoken in large rich countries.", | |
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| "sec_num": null | |
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| { | |
| "text": "This talk describes our recent work on developing unsupervised speech technology, where transcripts and pronunciation dictionaries are not used. The work is inspired by considering both how young infants may begin to acquire the sounds and words of their language, and how we might develop systems to help linguists analyze and document endangered languages. I will first present work on learning from speech audio alone, where the system must learn to segment the speech stream into word tokens and cluster repeated instances of the same word together to learn a lexicon of vocabulary items. The approach combines Bayesian and neural network methods to address learning at the word and sub-word levels. Abstract: Simulated physical environments, with common physical laws, objects and agents with bodies, provide us with consistency to facilitate transfer and continual learning. In such environments, research topics such as learning to experiment, learning to learn and emergent communication can be easily explored. Given the relevance of these topics to language, it is natural to ask ourselves whether research in language would benefit from the development of such environments, and whether language can contribute toward improving such environments and agents within them. This talk will provide an overview of some of these environments, discuss learning to learn and its potential relevance to language, and present some deep reinforcement learning agents that capitalize on formal language instructions to develop disentangled interpretable representations that allow them to generalize to a wide variety of zero-shot semantic tasks. The talk will pose more questions than answers in the hope of stimulating discussion.", | |
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| "section": "Preface by the Program Committee Co-Chairs", | |
| "sec_num": null | |
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| { | |
| "text": "Bio: I was born in Zimbabwe, with malaria. I was a refugee from the war in Mocambique and thanks to my parents getting in debt to buy me a passport from a corrupt official, I grew up in Portugal without water and electricity, before the EU got there, and without my parents who were busy making money to pay their debt. At 8, I joined my parents in Venezuela and began school in the hood; see City of God. I moved to South Africa after high-school and sold beer illegally in black-townships for a living until 1991. Apartheid was the worst thing I ever experienced. I did my BSc in electrical engineering and MSc in control at the University of the Witwatersrand, where I strived to be the best student to prove to racists that anyone can do it. ", | |
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| "raw_text": "Saturday, September 9, 2017 (continued) 10:30-12:10 Session 1A: Syntax 1 10:30-10:55 Monolingual Phrase Alignment on Parse Forests Yuki Arase and Jun'ichi Tsujii 10:55-11:20 Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set Tianze Shi, Liang Huang and Lillian Lee 11:20-11:45 Parsing with Traces: An O(n\u02c64) Algorithm and a Structural Representation Jonathan K. Kummerfeld and Dan Klein 11:45-12:10 Quasi-Second-Order Parsing for 1-Endpoint-Crossing, Pagenumber-2 Graphs Junjie Cao, Sheng Huang, Weiwei Sun and Xiaojun Wan 10:30-12:10 Session 1B: Information Extraction 1 10:30-10:55 Position-aware Attention and Supervised Data Improve Slot Filling Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli and Christopher D. Man- ning 10:55-11:20 Heterogeneous Supervision for Relation Extraction: A Representation Learning Ap- proach Liyuan Liu, Xiang Ren, Qi Zhu, Shi Zhi, Huan Gui, Heng Ji and Jiawei Han 11:20-11:45 Integrating Order Information and Event Relation for Script Event Prediction Zhongqing Wang, Yue Zhang and Ching-Yun Chang 11:45-12:10 Entity Linking for Queries by Searching Wikipedia Sentences Chuanqi Tan, Furu Wei, Pengjie Ren, Weifeng Lv and Ming Zhou xxxviii Saturday, September 9, 2017 (continued) 10:30-12:10 Session 1C: Multilingual NLP 10:30-10:55 Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data Tommaso Pasini and Roberto Navigli 10:55-11:20 Universal Semantic Parsing Siva Reddy, Oscar T\u00e4ckstr\u00f6m, Slav Petrov, Mark Steedman and Mirella Lapata 11:20-11:45 Mimicking Word Embeddings using Subword RNNs Yuval Pinter, Robert Guthrie and Jacob Eisenstein 11:45-12:10 Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages Ehsaneddin Asgari and Hinrich Sch\u00fctze 10:30-12:10 Session 1D: Poster Session: Demo 12:10-13:40 Lunch 13:40-15:20 Session 2A: Machine Translation 1 13:40-14:05 Neural Machine Translation with Source-Side Latent Graph Parsing Kazuma Hashimoto and Yoshimasa Tsuruoka 14:05-14:30 Neural Machine Translation with Word Predictions Rongxiang Weng, Shujian Huang, Zaixiang Zheng, XIN-YU DAI and Jiajun CHEN 14:30-14:55 Towards Decoding as Continuous Optimisation in Neural Machine Translation Cong Duy Vu Hoang, Gholamreza Haffari and Trevor Cohn 14:55-15:20 Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Vi\u00e9gas, Martin Wattenberg, Greg Corrado, Macduff Hughes and Jeffrey Dean xxxix Saturday, September 9, 2017 (continued) 13:40-15:20 Session 2B: Language Grounding 13:40-14:05 Where is Misty? Interpreting Spatial Descriptors by Modeling Regions in Space Nikita Kitaev and Dan Klein 14:05-14:30 Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks Afshin Rahimi, Timothy Baldwin and Trevor Cohn 14:30-14:55 Colors in Context: A Pragmatic Neural Model for Grounded Language Understand- ing Will Monroe, Robert X. D. Hawkins, Noah D. Goodman and Christopher Potts 14:55-15:20 Obj2Text: Generating Visually Descriptive Language from Object Layouts Xuwang Yin and Vicente Ordonez 13:40-15:20 Session 2C: Discourse and Summarization 13:40-14:05 End-to-end Neural Coreference Resolution Kenton Lee, Luheng He, Mike Lewis and Luke Zettlemoyer 14:05-14:30 Neural Net Models of Open-domain Discourse Coherence Jiwei Li and Dan Jurafsky 14:30-14:55 Affinity-Preserving Random Walk for Multi-Document Summarization Kexiang Wang, Tianyu Liu, Zhifang Sui and Baobao Chang 14:55-15:20 A Mention-Ranking Model for Abstract Anaphora Resolution Ana Marasovic, Leo Born, Juri Opitz and Anette Frank Saturday, September 9, 2017 (continued) 13:40-15:20 Session 2D: Poster Session. Embeddings Hierarchical Embeddings for Hypernymy Detection and Directionality Kim Anh Nguyen, Maximilian K\u00f6per, Sabine Schulte im Walde and Ngoc Thang Vu Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics Zhe Zhao, Tao Liu, Shen Li, Bofang Li and Xiaoyong Du", | |
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| "raw_text": "Sunday, September 10, 2017 (continued) 09:00-10:00 Plenary Session. Invited Talk by Sharon Goldwater 09:00-10:00 Towards more universal language technology: unsupervised learning from speech Sharon Goldwater 10:00-10:30 Coffee Break 10:30-12:10 Session 4A: Reading and Retrieving 10:30-10:55 A Structured Learning Approach to Temporal Relation Extraction Qiang Ning, Zhili Feng and Dan Roth 10:55-11:20 Importance sampling for unbiased on-demand evaluation of knowledge base popu- lation Arun Chaganty, Ashwin Paranjape, Percy Liang and Christopher D. Manning 11:20-11:45 PACRR: A Position-Aware Neural IR Model for Relevance Matching Kai Hui, Andrew Yates, Klaus Berberich and Gerard de Melo 11:45-12:10 Globally Normalized Reader Jonathan Raiman and John Miller 10:30-12:10 Session 4B: Multimodal NLP 2 10:30-10:55 Speech segmentation with a neural encoder model of working memory Micha Elsner and Cory Shain 10:55-11:20 Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain Luana Bulat, Stephen Clark and Ekaterina Shutova 11:20-11:45 Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video Haoran Li, Junnan Zhu, Cong Ma, Jiajun Zhang and Chengqing Zong 11:45-12:10 Tensor Fusion Network for Multimodal Sentiment Analysis Amir Zadeh, Minghai Chen, Soujanya Poria, Erik Cambria and Louis-Philippe Morency l", | |
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| "raw_text": "Sunday, September 10, 2017 (continued) 10:30-12:10 Session 4C: Human Centered NLP and Linguistic Theory 10:30-10:55 ConStance: Modeling Annotation Contexts to Improve Stance Classification Kenneth Joseph, Lisa Friedland, William Hobbs, David Lazer and Oren Tsur 10:55-11:20 Deeper Attention to Abusive User Content Moderation John Pavlopoulos, Prodromos Malakasiotis and Ion Androutsopoulos 11:20-11:45 Outta Control: Laws of Semantic Change and Inherent Biases in Word Representa- tion Models Haim Dubossarsky, Daphna Weinshall and Eitan Grossman 11:45-12:10 Human Centered NLP with User-Factor Adaptation Veronica Lynn, Youngseo Son, Vivek Kulkarni, Niranjan Balasubramanian and H. Andrew Schwartz Sunday, September 10, 2017 (continued) 15:50-17:30 Session 6D: Poster Session. Summarization, Generation, Dialog, and Discourse 1", | |
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| "raw_text": "Preserving Distributional Information in Dialogue Act Classification Quan Hung Tran, Ingrid Zukerman and Gholamreza Haffari Adversarial Learning for Neural Dialogue Generation Jiwei Li, Will Monroe, Tianlin Shi, S\u00e9bastien Jean, Alan Ritter and Dan Jurafsky Using Context Information for Dialog Act Classification in DNN Framework Yang Liu, Kun Han, Zhao Tan and Yun Lei Modeling Dialogue Acts with Content Word Filtering and Speaker Preferences Yohan Jo, Michael Yoder, Hyeju Jang and Carolyn Rose Towards Implicit Content-Introducing for Generative Short-Text Conversation Sys- tems Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao and Rui Yan Affordable On-line Dialogue Policy Learning Cheng Chang, Runzhe Yang, Lu Chen, Xiang Zhou and Kai Yu Generating High-Quality and Informative Conversation Responses with Sequence- to-Sequence Models Yuanlong Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope and Ray Kurzweil Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars Arash Eshghi, Igor Shalyminov and Oliver Lemon Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Re- inforcement Learning Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee and Kam-Fai Wong Why We Need New Evaluation Metrics for NLG Jekaterina Novikova, Ond\u0159ej Du\u0161ek, Amanda Cercas Curry and Verena Rieser Challenges in Data-to-Document Generation Sam Wiseman, Stuart Shieber and Alexander Rush", | |
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| "pages": "45--57", | |
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| "num": null, | |
| "urls": [], | |
| "raw_text": "All that is English may be Hindi: Enhancing language identification through auto- matic ranking of the likeliness of word borrowing in social media Jasabanta Patro, Bidisha Samanta, Saurabh Singh, Abhipsa Basu, Prithwish Mukherjee, Monojit Choudhury and Animesh Mukherjee Multi-View Unsupervised User Feature Embedding for Social Media-based Sub- stance Use Prediction Tao Ding, Warren K. Bickel and Shimei Pan Demographic-aware word associations Aparna Garimella, Carmen Banea and Rada Mihalcea A Factored Neural Network Model for Characterizing Online Discussions in Vector Space Hao Cheng, Hao Fang and Mari Ostendorf Dimensions of Interpersonal Relationships: Corpus and Experiments Farzana Rashid and Eduardo Blanco Argument Mining on Twitter: Arguments, Facts and Sources Mihai Dusmanu, Elena Cabrio and Serena Villata Distinguishing Japanese Non-standard Usages from Standard Ones Tatsuya Aoki, Ryohei Sasano, Hiroya Takamura and Manabu Okumura Connotation Frames of Power and Agency in Modern Films Maarten Sap, Marcella Cindy Prasettio, Ari Holtzman, Hannah Rashkin and Yejin Choi Controlling Human Perception of Basic User Traits Daniel Preo\u0163iuc-Pietro, Sharath Chandra Guntuku and Lyle Ungar Topic Signatures in Political Campaign Speeches Cl\u00e9ment Gautrais, Peggy Cellier, Ren\u00e9 Quiniou and Alexandre Termier Assessing Objective Recommendation Quality through Political Forecasting H. Andrew Schwartz, Masoud Rouhizadeh, Michael Bishop, Philip Tetlock, Bar- bara Mellers and Lyle Ungar Sunday, September 10, 2017 (continued) Never Abandon Minorities: Exhaustive Extraction of Bursty Phrases on Microblogs Using Set Cover Problem Masumi Shirakawa, Takahiro Hara and Takuya Maekawa 18:00-22:00 Social Event Monday, September 11, 2017 07:30-17:30 Registration Day 3 08:00-09:00 Morning Coffee 09:00-10:00 Plenary Session. Invited Talk by Dan Jurafsky 09:00-10:00 \"Does This Vehicle Belong to You\"? Processing the Language of Policing for Im- proving Police-Community Relations Dan Jurafsky 10:00-10:30 Coffee Break 10:30-12:10 Session 7A: Machine Learning 3 10:30-10:55 Maximum Margin Reward Networks for Learning from Explicit and Implicit Super- vision Haoruo Peng, Ming-Wei Chang and Wen-tau Yih 10:55-11:20 The Impact of Modeling Overall Argumentation with Tree Kernels Henning Wachsmuth, Giovanni Da San Martino, Dora Kiesel and Benno Stein 11:20-11:45 Learning Generic Sentence Representations Using Convolutional Neural Networks Zhe Gan, Yunchen Pu, Ricardo Henao, Chunyuan Li, Xiaodong He and Lawrence Carin 11:45-12:10 Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks Hadi Amiri, Timothy Miller and Guergana Savova", | |
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| }, | |
| "BIBREF474": { | |
| "ref_id": "b474", | |
| "title": "Deal or No Deal? End-to-End Learning of Negotiation Dialogues Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh and Dhruv Batra 10:55-11:20 Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy", | |
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| { | |
| "first": "Buzhou", | |
| "middle": [], | |
| "last": "Zhao", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Aleksandr", | |
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| "venue": "Part-of-Speech Tagging for Twitter with Adversarial Neural Networks Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng and Xuanjing Huang", | |
| "volume": "10", | |
| "issue": "", | |
| "pages": "45--57", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Monday, September 11, 2017 (continued) 10:30-12:10 Session 7B: Syntax 4 10:30-10:55 Part-of-Speech Tagging for Twitter with Adversarial Neural Networks Tao Gui, Qi Zhang, Haoran Huang, Minlong Peng and Xuanjing Huang 10:55-11:20 Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings Bofang Li, Tao Liu, Zhe Zhao, Buzhou Tang, Aleksandr Drozd, Anna Rogers and Xiaoyong Du 11:20-11:45 Does syntax help discourse segmentation? Not so much Chlo\u00e9 Braud, Oph\u00e9lie Lacroix and Anders S\u00f8gaard 11:45-12:10 Nonparametric Bayesian Semi-supervised Word Segmentation Ryo Fujii, Ryo Domoto and Daichi Mochihashi 10:30-12:10 Session 7C: Dialogue 10:30-10:55 Deal or No Deal? End-to-End Learning of Negotiation Dialogues Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh and Dhruv Batra 10:55-11:20 Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning Lu Chen, Xiang Zhou, Cheng Chang, Runzhe Yang and Kai Yu 11:20-11:45 Towards Debate Automation: a Recurrent Model for Predicting Debate Winners Peter Potash and Anna Rumshisky 11:45-12:10 Conversation Modeling on Reddit Using a Graph-Structured LSTM Victoria Zayats and Mari Ostendorf", | |
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| "title": "Machine Translation and Multilingual NLP 2 Joint Prediction of Word Alignment with Alignment Types Anahita Mansouri Bigvand, Te Bu and Anoop Sarkar Further Investigation into Reference Bias in Monolingual Evaluation of Machine Translation Qingsong Ma, Yvette Graham, Timothy Baldwin and Qun Liu A Challenge Set Approach to Evaluating Machine Translation Pierre Isabelle, Colin Cherry and George Foster Knowledge Distillation for Bilingual Dictionary Induction Ndapandula Nakashole and Raphael Flauger Machine Translation, it's a question of style, innit? The case of English tag questions Rachel Bawden Deciphering Related Languages Nima Pourdamghani and Kevin Knight Identifying Cognate Sets Across Dictionaries of Related Languages Adam St Arnaud, David Beck and Grzegorz Kondrak Learning Language Representations for Typology Prediction Chaitanya Malaviya, Graham Neubig and Patrick Littell Cheap Translation for Cross-Lingual Named Entity Recognition Stephen Mayhew, Chen-Tse Tsai and Dan Roth Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation Ivan Vuli\u0107", | |
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| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Monday, September 11, 2017 (continued) 10:30-12:10 Session 7D: Poster Session. Machine Translation and Multilingual NLP 2 Joint Prediction of Word Alignment with Alignment Types Anahita Mansouri Bigvand, Te Bu and Anoop Sarkar Further Investigation into Reference Bias in Monolingual Evaluation of Machine Translation Qingsong Ma, Yvette Graham, Timothy Baldwin and Qun Liu A Challenge Set Approach to Evaluating Machine Translation Pierre Isabelle, Colin Cherry and George Foster Knowledge Distillation for Bilingual Dictionary Induction Ndapandula Nakashole and Raphael Flauger Machine Translation, it's a question of style, innit? The case of English tag ques- tions Rachel Bawden Deciphering Related Languages Nima Pourdamghani and Kevin Knight Identifying Cognate Sets Across Dictionaries of Related Languages Adam St Arnaud, David Beck and Grzegorz Kondrak Learning Language Representations for Typology Prediction Chaitanya Malaviya, Graham Neubig and Patrick Littell Cheap Translation for Cross-Lingual Named Entity Recognition Stephen Mayhew, Chen-Tse Tsai and Dan Roth Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation Ivan Vuli\u0107, Nikola Mrk\u0161i\u0107 and Anna Korhonen Classification of telicity using cross-linguistic annotation projection Annemarie Friedrich and Damyana Gateva", | |
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| }, | |
| "BIBREF477": { | |
| "ref_id": "b477", | |
| "title": "10 Session 7F: Poster Session. NLP Applications Word Etymology as Native Language Interference Vivi Nastase and Carlo Strapparava A Simpler and More Generalizable Story Detector using Verb and Character Features Joshua Eisenberg and Mark Finlayson Multi-modular domain-tailored OCR post-correction Sarah Schulz and Jonas Kuhn Learning to Predict Charges for Criminal Cases with Legal Basis Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang and Dongyan Zhao Quantifying the Effects of Text Duplication on Semantic Models Alexandra Schofield, Laure Thompson and David Mimno Identifying Semantically Deviating Outlier Documents Honglei Zhuang, Chi Wang, Fangbo Tao, Lance Kaplan and Jiawei Han Detecting and Explaining Causes From Text For a Time Series Event Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen and Eduard Hovy Monday", | |
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| "first": "Kris", | |
| "middle": [], | |
| "last": "Ganjam", | |
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| "first": "Navendu", | |
| "middle": [], | |
| "last": "Jain", | |
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| }, | |
| { | |
| "first": "Jessica", | |
| "middle": [], | |
| "last": "Lundin", | |
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| }, | |
| { | |
| "first": "Ryen", | |
| "middle": [], | |
| "last": "White", | |
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| }, | |
| { | |
| "first": "Jimmy Lin ; Rui", | |
| "middle": [], | |
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| { | |
| "first": "Masao", | |
| "middle": [], | |
| "last": "Utiyama", | |
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| }, | |
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| "year": 2010, | |
| "venue": "Entity Linking via Joint Encoding of Types, Descriptions, and Context Nitish Gupta, Sameer Singh and Dan Roth An Insight Extraction System on BioMedical Literature with Deep Neural Networks Hua He", | |
| "volume": "10", | |
| "issue": "", | |
| "pages": "55--70", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks Peng-Hsuan Li, Ruo-Ping Dong, Yu-Siang Wang, Ju-Chieh Chou and Wei-Yun Ma Fast and Accurate Entity Recognition with Iterated Dilated Convolutions Emma Strubell, Patrick Verga, David Belanger and Andrew McCallum Entity Linking via Joint Encoding of Types, Descriptions, and Context Nitish Gupta, Sameer Singh and Dan Roth An Insight Extraction System on BioMedical Literature with Deep Neural Networks Hua He, Kris Ganjam, Navendu Jain, Jessica Lundin, Ryen White and Jimmy Lin 10:30-12:10 Session 7F: Poster Session. NLP Applications Word Etymology as Native Language Interference Vivi Nastase and Carlo Strapparava A Simpler and More Generalizable Story Detector using Verb and Character Fea- tures Joshua Eisenberg and Mark Finlayson Multi-modular domain-tailored OCR post-correction Sarah Schulz and Jonas Kuhn Learning to Predict Charges for Criminal Cases with Legal Basis Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang and Dongyan Zhao Quantifying the Effects of Text Duplication on Semantic Models Alexandra Schofield, Laure Thompson and David Mimno Identifying Semantically Deviating Outlier Documents Honglei Zhuang, Chi Wang, Fangbo Tao, Lance Kaplan and Jiawei Han Detecting and Explaining Causes From Text For a Time Series Event Dongyeop Kang, Varun Gangal, Ang Lu, Zheng Chen and Eduard Hovy Monday, September 11, 2017 (continued) 14:40-14:55 Image Pivoting for Learning Multilingual Multimodal Representations Spandana Gella, Rico Sennrich, Frank Keller and Mirella Lapata 14:55-15:10 Neural Machine Translation with Source Dependency Representation Kehai Chen, Rui Wang, Masao Utiyama, Lemao Liu, Akihiro Tamura, Eiichiro Sumita and Tiejun Zhao 15:10-15:25 Visual Denotations for Recognizing Textual Entailment Dan Han, Pascual Mart\u00ednez-G\u00f3mez and Koji Mineshima 13:40-15:25 Session 8B: Machine Learning (Short) 13:40-13:55 Sequence Effects in Crowdsourced Annotations Nitika Mathur, Timothy Baldwin and Trevor Cohn 13:55-14:10 No Need to Pay Attention: Simple Recurrent Neural Networks Work! Ferhan Ture and Oliver Jojic 14:10-14:25 The strange geometry of skip-gram with negative sampling David Mimno and Laure Thompson 14:25-14:40 Natural Language Processing with Small Feed-Forward Networks Jan A. Botha, Emily Pitler, Ji Ma, Anton Bakalov, Alex Salcianu, David Weiss, Ryan McDonald and Slav Petrov 14:40-14:55 Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction Xin Li and Wai Lam 14:55-15:10 Analogs of Linguistic Structure in Deep Representations Jacob Andreas and Dan Klein 15:10-15:25 A Simple Regularization-based Algorithm for Learning Cross-Domain Word Em- beddings Wei Yang, Wei Lu and Vincent Zheng", | |
| "links": null | |
| }, | |
| "BIBREF478": { | |
| "ref_id": "b478", | |
| "title": ":40 Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia E. Dario Gutierrez, Guillermo Cecchi", | |
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| "venue": "25 Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora Hainan Xu and Philipp Koehn", | |
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| "pages": "25--42", | |
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| "num": null, | |
| "urls": [], | |
| "raw_text": "Monday, September 11, 2017 (continued) 13:40-15:25 Session 8C: NLP Applications (Short) 13:40-13:55 Learning what to read: Focused machine reading Enrique Noriega-Atala, Marco A. Valenzuela-Esc\u00e1rcega, Clayton Morrison and Mi- hai Surdeanu 13:55-14:10 DOC: Deep Open Classification of Text Documents Lei Shu, Hu Xu and Bing Liu 14:10-14:25 Charmanteau: Character Embedding Models For Portmanteau Creation Varun Gangal, Harsh Jhamtani, Graham Neubig, Eduard Hovy and Eric Nyberg 14:25-14:40 Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia E. Dario Gutierrez, Guillermo Cecchi, Cheryl Corcoran and Philip Corlett 14:40-14:55 Truth of Varying Shades: Analyzing Language in Fake News and Political Fact- Checking Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova and Yejin Choi 14:55-15:10 Topic-Based Agreement and Disagreement in US Electoral Manifestos Stefano Menini, Federico Nanni, Simone Paolo Ponzetto and Sara Tonelli 15:10-15:25 Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Par- allel Corpora Hainan Xu and Philipp Koehn 15:25-15:50 Coffee Break Monday, September 11, 2017 (continued) 15:50-17:25 Plenary Session. Best Paper 17:00-17:25 Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Con- cept Maps Tobias Falke and Iryna Gurevych 16:20-16:35 Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog Satwik Kottur, Jos\u00e9 Moura, Stefan Lee and Dhruv Batra 16:35-17:00 Depression and Self-Harm Risk Assessment in Online Forums Andrew Yates, Arman Cohan and Nazli Goharian 15:55-16:20 Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez and Kai-Wei Chang 17:25-17:45 Plenary Session. Closing Remarks 17:25-17:45 Closing Remarks General Chair lxxiii", | |
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| "text": "Dan Jurafsky is Professor and Chair of Linguistics and Professor of Computer Science, at Stanford University. His research has focused on the extraction of meaning, intention, and affect from text and speech, on the processing of Chinese, and on applying natural language processing to the cognitive and social sciences. Dan's deep interest in NLP education led him to co-write with Jim Martin the widely-used textbook \"Speech and Language Processing\" (whose 3rd edition is in (slow) progress) and co-teach with Chris Manning the first massive open online class on natural language processing. Dan was the recipient of the 2002 MacArthur Fellowship and is a 2015 James Beard Award Nominee for his book, \"The Language of Food: A Linguist Reads the Menu\".", | |
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| "text": "Sharon Goldwater is a Reader at the University of Edinburgh's School of Informatics, where she is a member of the Institute for Language, Cognition and Computation. She received her PhD in 2007 from Brown University and spent two years as a postdoctoral researcher at Stanford University before moving to Edinburgh. Her research interests include unsupervised learning for speech and language processing, computer modelling of language acquisition in children, and computational studies of language use. Dr. Goldwater co-chaired the 2014 Conference of the European Chapter of the Association for Computational Linguistics and is Chair-Elect of EACL. She has served on the editorial boards of the Transactions of the Association for Computational Linguistics, the Computational Linguistics journal, and OPEN MIND: Advances in Cognitive Science (a new open-access journal). In 2016, she received the Roger Needham Award from the British Computer Society, awarded for \"distinguished research contribution in computer science by a UK-based researcher who has completed up to 10 years of postdoctoral research.\" Invited Speaker: Nando de Freitas, Google Deepmind Physical simulation, learning and language", | |
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| "text": "I did my PhD on Bayesian methods for neural networks at Trinity College, Cambridge University. I did a postdoc in Artificial Intelligence at UC Berkeley. I became a Full Professor at the University of British Columbia, before joining the University of Oxford in 2013. I quit Oxford in 2017 to join DeepMind full-time, where I lead the Machine Learning team. I aim to solve intelligence so that future generations have a better life. I have been a Senior Fellow of the Canadian Institute for Advanced Research for a long time. Some of my recent awards, mostly thanks to my collaborators, include: Best Paper Award at the International Conference on Machine Learning (2016), Best Paper Award at the International Conference on Learning Representations (2016), Winner of round 5 of the Yelp Dataset Challenge (2015), Distinguished Paper Award at the International Joint Conference on Artificial Intelligence (2013), Charles A. McDowell Award for Excellence in Research (2012), and Mathematics of Information Technology and Complex Systems Young Researcher Award (2010).", | |
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| "text": "Monolingual Phrase Alignment on Parse Forests Yuki Arase and Jun'ichi Tsujii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set Tianze Shi, Liang Huang and Lillian Lee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .", | |
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