Bridging Resolution: A Survey of the State of the Art

Hideo Kobayashi, Vincent Ng


Abstract
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.
Anthology ID:
2020.coling-main.331
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3708–3721
Language:
URL:
https://aclanthology.org/2020.coling-main.331
DOI:
10.18653/v1/2020.coling-main.331
Bibkey:
Cite (ACL):
Hideo Kobayashi and Vincent Ng. 2020. Bridging Resolution: A Survey of the State of the Art. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3708–3721, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Bridging Resolution: A Survey of the State of the Art (Kobayashi & Ng, COLING 2020)
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PDF:
https://preview.aclanthology.org/auto-file-uploads/2020.coling-main.331.pdf