Coreference Resolution without Span Representations

Yuval Kirstain, Ori Ram, Omer Levy


Abstract
The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers. An exception to this trend is coreference resolution, where a sophisticated task-specific model is appended to a pretrained transformer encoder. While highly effective, the model has a very large memory footprint – primarily due to dynamically-constructed span and span-pair representations – which hinders the processing of complete documents and the ability to train on multiple instances in a single batch. We introduce a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, and heuristics. Our model performs competitively with the current standard model, while being simpler and more efficient.
Anthology ID:
2021.acl-short.3
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14–19
Language:
URL:
https://aclanthology.org/2021.acl-short.3
DOI:
10.18653/v1/2021.acl-short.3
Bibkey:
Cite (ACL):
Yuval Kirstain, Ori Ram, and Omer Levy. 2021. Coreference Resolution without Span Representations. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 14–19, Online. Association for Computational Linguistics.
Cite (Informal):
Coreference Resolution without Span Representations (Kirstain et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.3.pdf
Video:
 https://aclanthology.org/2021.acl-short.3.mp4
Code
 yuvalkirstain/s2e-coref
Data
CoNLLCoNLL-2012GAP Coreference Dataset