ACL 2023long1 citations
PairSpanBERT: An Enhanced Language Model for Bridging Resolution
Hideo Kobayashi, Yufang Hou, Vincent Ng
Abstract
We present PairSpanBERT, a SpanBERT-based pre-trained model specialized for bridging resolution. To this end, we design a novel pre-training 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
@inproceedings{kobayashi-etal-2023-pairspanbert,
title = "{P}air{S}pan{BERT}: An Enhanced Language Model for Bridging Resolution",
author = "Kobayashi, Hideo and
Hou, Yufang and
Ng, Vincent",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.383/",
doi = "10.18653/v1/2023.acl-long.383",
pages = "6931--6946"
}