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)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.331.pdf
- Data
- BASHI, ISNotes