Long-distant Coreference Resolution by Clustering-extended BERT for Korean and English Document

Cheolhun Heo, Kuntae Kim, Key-sun Choi

http://doi.org/10.5626/JOK.2020.47.12.1126

Coreference resolution is a natural language processing task of identifying all mentions that refer to the same denotation in the given natural language document. It contributes to improving the performance of various natural language processing tasks by resolving the co-referents caused by linguistically replaceable realizations by using the referencible forms such as pronouns, indicative adjectives, and abbreviations but preventing co-referencing of homonyms (i.e., same form but different meaning). We propose a novel approach to coreference resolution particulary to identify the long-distant co-referents by applying long-distance clustering for surface forms under a BERT-based model performing well in English. We compare the performance of the proposed model and other models over the Korean and English datasets. Results demonstrated that our model has a better grasp of contextual elements compared to the other models.