| { | |
| "paper_id": "2020", | |
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| "date_generated": "2023-01-19T15:34:49.927267Z" | |
| }, | |
| "title": "", | |
| "authors": [ | |
| { | |
| "first": "Sweta", | |
| "middle": [], | |
| "last": "Agrawal", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Parag", | |
| "middle": [], | |
| "last": "Jain", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
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| "abstract": "", | |
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| "paper_id": "2020", | |
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| "abstract": [], | |
| "body_text": [ | |
| { | |
| "text": "Welcome to the Fourth Workshop on Structured Prediction for NLP! Structured prediction has a strong tradition within the natural language processing (NLP) community, owing to the discrete, compositional nature of words and sentences, which leads to natural combinatorial representations such as trees, sequences, segments, or alignments, among others. It is no surprise that structured output models have been successful and popular in NLP applications since their inception. Many other NLP tasks, including, but not limited to: semantic parsing, slot filling, machine translation, or information extraction, are commonly modeled as structured problems, and accounting for said structure has often lead to performance gain.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Of late, continuous representation learning via neural networks has been a significant complementary direction, leading to improvements in unsupervised and semi-supervised pre-training, transfer learning, domain adaptation, etc. Using word embeddings as features for structured models such as part-of-speech taggers count among the very first uses of continuous embeddings in NLP, and the symbiosis between the two approaches is an exciting research direction today.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This year we received 26 submissions and, after double-blind peer review, 16 were accepted (4 of which are non-archival papers) for presentation in this edition of the workshop, all exploring this interplay between structure and neural data representations, from different, important points of view. The program includes work on structure-informed representation learning, energy-based learning, and structured fine-tuning of language models. Our program also includes six invited presentations from influential researchers.", | |
| "cite_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Our warmest thanks go to the program committee -for their time and effort providing valuable feedback, to all submitting authors -for their thought-provoking work, and to the invited speakers -for doing us the honor of joining our program. We are looking forward to seeing you online! Priyanka Agrawal Zornitsa Kozareva Julia Kreutzer Gerasimos Lampouras Andr\u00e9 Martins Sujith Ravi Andreas Vlachos", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
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| } | |
| } |