Zero Pronouns Identification based on Span prediction

Sei Iwata, Taro Watanabe, Masaaki Nagata


Abstract
The presence of zero-pronoun (ZP) greatly affects the downstream tasks of NLP in pro-drop languages such as Japanese and Chinese. To tackle the problem, the previous works identified ZPs as sequence labeling on the word sequence or the linearlized tree nodes of the input. We propose a novel approach to ZP identification by casting it as a query-based argument span prediction task. Given a predicate as a query, our model predicts the omission with ZP. In the experiments, our model surpassed the sequence labeling baseline.
Anthology ID:
2021.acl-srw.34
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
331–336
Language:
URL:
https://aclanthology.org/2021.acl-srw.34
DOI:
10.18653/v1/2021.acl-srw.34
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-srw.34.pdf
Optional supplementary material:
 2021.acl-srw.34.OptionalSupplementaryMaterial.pdf