TY - GEN
T1 - A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution
AU - Lai, Tuan
AU - Ji, Heng
AU - Bui, Trung
AU - Tran, Quan Hung
AU - Dernoncourt, Franck
AU - Chang, Walter
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - Event coreference resolution is an important research problem with many applications. Despite the recent remarkable success of pretrained language models, we argue that it is still highly beneficial to utilize symbolic features for the task. However, as the input for coreference resolution typically comes from upstream components in the information extraction pipeline, the automatically extracted symbolic features can be noisy and contain errors. Also, depending on the specific context, some features can be more informative than others. Motivated by these observations, we propose a novel context-dependent gated module to adaptively control the information flows from the input symbolic features. Combined with a simple noisy training method, our best models achieve state-of-the-art results on two datasets: ACE 2005 and KBP 2016.
AB - Event coreference resolution is an important research problem with many applications. Despite the recent remarkable success of pretrained language models, we argue that it is still highly beneficial to utilize symbolic features for the task. However, as the input for coreference resolution typically comes from upstream components in the information extraction pipeline, the automatically extracted symbolic features can be noisy and contain errors. Also, depending on the specific context, some features can be more informative than others. Motivated by these observations, we propose a novel context-dependent gated module to adaptively control the information flows from the input symbolic features. Combined with a simple noisy training method, our best models achieve state-of-the-art results on two datasets: ACE 2005 and KBP 2016.
UR - http://www.scopus.com/inward/record.url?scp=85108564545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85108564545&partnerID=8YFLogxK
U2 - 10.18653/v1/2021.naacl-main.274
DO - 10.18653/v1/2021.naacl-main.274
M3 - Conference contribution
AN - SCOPUS:85108564545
T3 - NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
SP - 3491
EP - 3499
BT - NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021
Y2 - 6 June 2021 through 11 June 2021
ER -