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| "paper_id": "2021", | |
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| "first": "Chen", | |
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| "text": "Many Asian countries are rapidly growing these days and the importance of communicating and exchanging the information with these countries has intensified. To satisfy the demand for communication among these countries, machine translation technology is essential.", | |
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| "section": "Preface", | |
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| "text": "Machine translation technology has rapidly evolved recently and it is seeing practical use especially between European languages. However, the translation quality of Asian languages is not that high compared to that of European languages, and machine translation technology for these languages has not reached a stage of proliferation yet. This is not only due to the lack of the language resources for Asian languages but also due to the lack of techniques to correctly transfer the meaning of sentences from/to Asian languages. Consequently, a place for gathering and sharing the resources and knowledge about Asian language translation is necessary to enhance machine translation research for Asian languages.", | |
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| "section": "Preface", | |
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| "text": "The Conference on Machine Translation (WMT), the world's largest machine translation workshop, mainly targets on European language. The International Workshop on Spoken Language Translation (IWSLT) has spoken language translation tasks for some Asian languages using TED talk data, but there is no task for written language. The Workshop on Asian Translation (WAT) is an open machine translation evaluation campaign focusing on Asian languages. WAT gathers and shares the resources and knowledge of Asian language translation to understand the problems to be solved for the practical use of machine translation technologies among all Asian countries. WAT is unique in that it is an \"open innovation platform\": the test data is fixed and open, so participants can repeat evaluations on the same data and confirm changes in translation accuracy over time. WAT has no deadline for the automatic translation quality evaluation (continuous evaluation), so participants can submit translation results at any time.", | |
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| "text": "Following the success of the previous WAT workshops (WAT2014 -WAT2020), WAT2021 will bring together machine translation researchers and users to try, evaluate, share and discuss brand-new ideas about machine translation. For the 8th WAT, we included several new translation tasks including Malayalam Visual Genome Task, MultiIndicMT, Restricted Translation Task and Ambiguous MSCOCO Task. We had 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation. About 2,100 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated. In addition to the shared tasks, WAT2021 also features research papers on topics related to machine translation, especially for Asian languages. The program committee accepted 5 research papers. ", | |
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| "text": "We are grateful to \"SunFlare Co., Ltd.\", \"Kawamura International\" and \"Asia-Pacific Association for Machine Translation (AAMT)\" for partially sponsoring the workshop. We would like to thank all the authors who submitted papers. We express our deepest gratitude to the committee members for their timely reviews. We also thank the ACL-IJCNLP 2021 organizers for their help with administrative matters.", | |
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| "section": "acknowledgement", | |
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| "text": "Overview of the 8th Workshop on Asian TranslationToshiaki Nakazawa, Hideki Nakayama, Chenchen Ding, Raj Dabre, Shohei Higashiyama, Hideya Mino, Isao Goto, Win Pa Pa, AnoopKunchukuttan, Shantipriya Parida, Ond\u0159ej Bojar, Chenhui Chu, Akiko Eriguchi, Kaori Abe, Yusuke Oda and Sadao Kurohashi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NHK's Lexically-Constrained Neural Machine Translation at WAT 2021 Hideya Mino, Kazutaka Kinugawa, Hitoshi Ito, Isao Goto, Ichiro Yamada and Takenobu Tokunaga 46 Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021 Katsuki Chousa and Makoto Morishita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 NICT's Neural Machine Translation Systems for the WAT21 Restricted Translation Task Zuchao Li, Masao Utiyama, Eiichiro Sumita and Hai Zhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Ponrudee Netisopakul and Thepchai Supnithi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Hybrid Statistical Machine Translation for English-Myanmar: UTYCC Submission to WAT-2021 Ye Kyaw Thu, Thazin Myint Oo, Hlaing Myat Nwe, Khaing Zar Mon, Nang Aeindray Kyaw, Naing Linn Phyo, Nann Hwan Khun and Hnin Aye Thant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 NICT-2 Translation System at WAT-2021: Applying a Pretrained Multilingual Encoder-Decoder Model to Low-resource Language Pairs Kenji Imamura and Eiichiro Sumita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Rakuten's Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation Raymond Hendy Susanto, Dongzhe Wang, Sunil Yadav, Mausam Jain and Ohnmar Htun . . . . . . 96 BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text chanjun park, Jaehyung Seo, Seolhwa Lee, Chanhee Lee, Hyeonseok Moon, Sugyeong Eo and Heuiseok Lim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Zero-pronoun Data Augmentation for Japanese-to-English Translation Ryokan Ri, Toshiaki Nakazawa and Yoshimasa Tsuruoka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Evaluation Scheme of Focal Translation for Japanese Partially Amended Statutes Takahiro Yamakoshi, Takahiro Komamizu, Yasuhiro Ogawa and Katsuhiko Toyama . . . . . . . . . . 124 TMU NMT System with Japanese BART for the Patent task of WAT 2021 Hwichan Kim and Mamoru Komachi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 System Description for Transperfect Wiktor Stribi\u017cew, Fred Bane, Jos\u00e9 Concei\u00e7\u00e3o and Anna Zaretskaya . . . . . . . . . . . . . . . . . . . . . . . . . 138", | |
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| "content": "<table><tr><td>Machine</td></tr></table>" | |
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