Parallel Data Helps Neural Entity Coreference Resolution

Gongbo Tang, Christian Hardmeier

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

Abstract

Coreference resolution is the task of finding expressions that refer to the same entity in a text. Coreference models are generally trained on monolingual annotated data but annotating coreference is expensive and challenging. Hardmeier et al. (2013) have shown that parallel data contains latent anaphoric knowledge, but it has not been explored in end-to-end neural models yet. In this paper, we propose a simple yet effective model to exploit coreference knowledge from parallel data. In addition to the conventional modules learning coreference from annotations, we introduce an unsupervised module to capture cross-lingual coreference knowledge. Our proposed cross-lingual model achieves consistent improvements, up to 1.74 percentage points, on the OntoNotes 5.0 English dataset using 9 different synthetic parallel datasets. These experimental results confirm that parallel data can provide additional coreference knowledge which is beneficial to coreference resolution tasks.
OriginalsprogEngelsk
TitelFindings of the Association for Computational Linguistics: ACL 2023
RedaktørerAnna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
UdgivelsesstedCanada
ForlagAssociation for Computational Linguistics
Publikationsdatojul. 2023
Udgave2023
Sider3162-3171
ISBN (Elektronisk)978-1-959429-62-3
DOI
StatusUdgivet - jul. 2023

Citationsformater