Abstract
In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference.- Anthology ID:
- 2020.codi-1.4
- Volume:
- Proceedings of the First Workshop on Computational Approaches to Discourse
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Chloé Braud, Christian Hardmeier, Junyi Jessy Li, Annie Louis, Michael Strube
- Venue:
- CODI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 32–41
- Language:
- URL:
- https://aclanthology.org/2020.codi-1.4
- DOI:
- 10.18653/v1/2020.codi-1.4
- Cite (ACL):
- Patrick Kahardipraja, Olena Vyshnevska, and Sharid Loáiciga. 2020. Exploring Span Representations in Neural Coreference Resolution. In Proceedings of the First Workshop on Computational Approaches to Discourse, pages 32–41, Online. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Span Representations in Neural Coreference Resolution (Kahardipraja et al., CODI 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.codi-1.4.pdf
- Code
- pkhdipraja/exploring-span-representations