Bridging Resolution: Making Sense of the State of the Art

Hideo Kobayashi and Vincent Ng.
Proceedings of the 2021 North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1652-1659, 2021.

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Abstract

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.

BibTeX entry

@InProceedings{Kobayashi+Ng:21a,
  author = {Hideo Kobayashi and Vincent Ng},
  title = {Bridging Resolution: Making Sense of the State of the Art},
  booktitle = {Proceedings of the 2021 North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages = {1652--1659}, 
  year = 2021}