| { | |
| "paper_id": "2021", | |
| "header": { | |
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| "date_generated": "2023-01-19T15:34:29.602610Z" | |
| }, | |
| "title": "", | |
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| "year": "", | |
| "venue": null, | |
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| "abstract": "", | |
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| "abstract": [], | |
| "body_text": [ | |
| { | |
| "text": "Welcome to the Fifth 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": "This workshop follows the four previous successful editions in 2020, 2019, 2017 and 2016 on Structured Prediction for NLP, as well as the closely related ICML 17 Workshop on Deep Structured Prediction. This year we received 18 submissions and, after double-blind peer review, 10 were accepted (2 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, leveraging structure in problems like parsing, hierarchical classification, etc. and structured feedback for sequence-to-sequence models. Our program also includes six invited presentations from influential researchers.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
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| { | |
| "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.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We are profoundly saddened by the loss of Arzoo Katiyar, who was our beloved program committee member since many previous editions. Our deepest condolences to her family and friends.", | |
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| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Organizing Committee Zornitsa Kozareva, Facebook AI, USA Sujith Ravi, SliceX AI, USA Priyanka Agrawal, Booking.com, Netherlands Andr\u00e9 F. T. Martins, Instituto Superior T\u00e9cnico, Instituto de Telecomunica\u00e7\u00f5es, and Unbabel, Portugal Andreas Vlachos, University of Cambridge, UK", | |
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| "section": "Zornitsa Kozareva Sujith Ravi Priyanka Agrawal Andr\u00e9 Martins Andreas Vlachos", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
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| "title": "Opening Remarks", | |
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| "raw_text": "Opening Remarks 09:10-09:50 Invited Talk 1", | |
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| "title": "Poster Session I RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation Jacob Parnell", | |
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| { | |
| "first": "Carolin", | |
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| "venue": "Inigo Jauregi Unanue and Massimo Piccardi SmBoP: Semi-autoregressive Bottom-up Semantic Parsing Ohad Rubin and Jonathan Berant", | |
| "volume": "11", | |
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| "pages": "40--52", | |
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| "raw_text": "Carolin Lawrence (NEC Labs Europe, Germany) 10:30-10:40 Break 10:40-11:40 Poster Session I RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation Jacob Parnell, Inigo Jauregi Unanue and Massimo Piccardi SmBoP: Semi-autoregressive Bottom-up Semantic Parsing Ohad Rubin and Jonathan Berant 11:40-12:20 Invited Talk 3", | |
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| "title": "50 Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks Julia Kreutzer", | |
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| "volume": "12", | |
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| "pages": "35--47", | |
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| "raw_text": "Rada Mihalcea (University of Michigan, USA) 12:20-12:35 Learning compositional structures for semantic graph parsing Jonas Groschwitz, Meaghan Fowlie and Alexander Koller 12:35-12:50 Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to- Sequence Tasks Julia Kreutzer, Stefan Riezler and Carolin Lawrence 12:50-13:50 Break 13:50-14:30 Invited Talk 4", | |
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| "ref_id": "b3", | |
| "title": ":45 Poster Session II Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification Erenay Dayanik", | |
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| { | |
| "first": "Ji", | |
| "middle": [], | |
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| ], | |
| "year": 2015, | |
| "venue": "30 Comparing Span Extraction Methods for Semantic Role Labeling Zhisong Zhang", | |
| "volume": "14", | |
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| "pages": "30--47", | |
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| "raw_text": "Heng Ji (University of Illinois Urbana-Champaign, USA) 6th August 2021 (continued) 14:30-14:45 Mode recovery in neural autoregressive sequence modeling Ilia Kulikov, Sean Welleck and Kyunghyun Cho 14:45-15:45 Poster Session II Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification Erenay Dayanik, Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa and Sebastian Pad\u00f3 A Globally Normalized Neural Model for Semantic Parsing Chenyang Huang, Wei Yang, Yanshuai Cao, Osmar Za\u00efane and Lili Mou 15:45-16:15 Break 16:15-16:30 Comparing Span Extraction Methods for Semantic Role Labeling Zhisong Zhang, Emma Strubell and Eduard Hovy 16:30-17:10 Invited Talk 5", | |
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| "first": "Scott", | |
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| { | |
| "first": "Yih", | |
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| "volume": "17", | |
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| "pages": "10--17", | |
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| "raw_text": "Scott Wen-tau Yih (Facebook AI Research, USA) 17:10-17:20 Closing Remarks x", | |
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