PairSpanBERT: An Enhanced Language Model for Bridging Resolution

Hideo Kobayashi, Yufang Hou and Vincent Ng.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 2023.

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Abstract

We present PairSpanBERT, a SpanBERT-based pre-trained model specialized for bridging resolution. PairSpanBERT is pre-trained with a novel objective that aims to learn the contexts in which two mentions are implicitly linked to each other from a large amount of data automatically generated either heuristically or via distance supervision with a knowledge graph. Despite the noise inherent in the automatically generated data, we achieve the best results reported to date on three evaluation datasets for bridging resolution when replacing SpanBERT with PairSpanBERT in a state-of-the-art resolver that jointly performs entity coreference resolution and bridging resolution.

BibTeX entry

@InProceedings{Kobayashi+etal:23a,
  author = {Hideo Kobayashi and Yufang Hou and Vincent Ng},
  title = {PairSpanBERT: An Enhanced Language Model for Bridging Resolution},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  
  year = 2023}