Multi-Task Learning for Japanese Predicate Argument Structure Analysis

Hikaru Omori, Mamoru Komachi


Abstract
An event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered state-of-the-art, ignore event-nouns or build a single model for solving both Japanese predicate argument structure analysis (PASA) and event-noun argument structure analysis (ENASA). However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. To address this problem, we present a multi-task learning method for PASA and ENASA. Our multi-task models improved the performance of both tasks compared to a single-task model by sharing knowledge from each task. Moreover, in PASA, our models achieved state-of-the-art results in overall F1 scores on the NAIST Text Corpus. In addition, this is the first work to employ neural networks in ENASA.
Anthology ID:
N19-1344
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3404–3414
Language:
URL:
https://aclanthology.org/N19-1344
DOI:
10.18653/v1/N19-1344
Bibkey:
Cite (ACL):
Hikaru Omori and Mamoru Komachi. 2019. Multi-Task Learning for Japanese Predicate Argument Structure Analysis. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3404–3414, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Multi-Task Learning for Japanese Predicate Argument Structure Analysis (Omori & Komachi, NAACL 2019)
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PDF:
https://aclanthology.org/N19-1344.pdf