Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis
Norio Takahashi, Tomohide Shibata, Daisuke Kawahara, Sadao Kurohashi
Abstract
To improve the accuracy of predicate-argument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable. We focus on a specific domain, specifically Japanese blogs on driving, and construct two wide-coverage datasets as a form of QA using crowdsourcing: a PAS-QA dataset and a reading comprehension QA (RC-QA) dataset. We train a machine comprehension (MC) model based on these datasets to perform PAS analysis. Our experiments show that a stepwise training method is the most effective, which pre-trains an MC model based on the RC-QA dataset to acquire domain knowledge and then fine-tunes based on the PAS-QA dataset.- Anthology ID:
- D19-5814
- Volume:
- Proceedings of the 2nd Workshop on Machine Reading for Question Answering
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 98–104
- Language:
- URL:
- https://aclanthology.org/D19-5814
- DOI:
- 10.18653/v1/D19-5814
- Cite (ACL):
- Norio Takahashi, Tomohide Shibata, Daisuke Kawahara, and Sadao Kurohashi. 2019. Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 98–104, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis (Takahashi et al., 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-5814.pdf