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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/N19-1344.pdf