Hideo Kobayashi


2022

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Constrained Multi-Task Learning for Bridging Resolution
Hideo Kobayashi | Yufang Hou | Vincent Ng
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pre-train the entity coreference model in the multi-task framework on the large amount of publicly available coreference data; and (3) integrating prior knowledge encoded in rule-based resolvers. Our approach achieves state-of-the-art results on three standard evaluation corpora.

2021

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Bridging Resolution: Making Sense of the State of the Art
Hideo Kobayashi | Vincent Ng
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.

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Neural Anaphora Resolution in Dialogue
Hideo Kobayashi | Shengjie Li | Vincent Ng
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.

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The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis Resolution in Dialogue: A Cross-Team Analysis
Shengjie Li | Hideo Kobayashi | Vincent Ng
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

The CODI-CRAC 2021 shared task is the first shared task that focuses exclusively on anaphora resolution in dialogue and provides three tracks, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. We perform a cross-task analysis of the systems that participated in the shared task in each of these tracks.

2020

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Bridging Resolution: A Survey of the State of the Art
Hideo Kobayashi | Vincent Ng
Proceedings of the 28th International Conference on Computational Linguistics

Bridging reference resolution is an anaphora resolution task that is arguably more challenging and less studied than entity coreference resolution. Given that significant progress has been made on coreference resolution in recent years, we believe that bridging resolution will receive increasing attention in the NLP community. Nevertheless, progress on bridging resolution is currently hampered in part by the scarcity of large annotated corpora for model training as well as the lack of standardized evaluation protocols. This paper presents a survey of the current state of research on bridging reference resolution and discusses future research directions.