Exploring Span Representations in Neural Coreference Resolution

Kahardipraja, Patrick and Vyshnevska, Olena and Loáiciga, Sharid

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.

In Proceedings of the First Workshop on Computational Approaches to Discourse , 2020
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@inproceedings{Kahardipraja-2020,
  title = {Exploring Span Representations in Neural Coreference Resolution},
  author = {Kahardipraja, Patrick and Vyshnevska, Olena and Lo{\'a}iciga, Sharid},
  booktitle = {Proceedings of the First Workshop on Computational Approaches to Discourse},
  month = nov,
  year = {2020},
  address = {Online},
  publisher = {Association for Computational Linguistics},
  url = {https://www.aclweb.org/anthology/2020.codi-1.4},
  doi = {10.18653/v1/2020.codi-1.4},
  pages = {32--41},
  topics = {},
  domains = {},
  approach = {},
  project = {}
}