Machine comprehension improves domain-specific japanese predicate-argument structure analysis

Norio Takahashi, Tomohide Shibata, Daisuke Kawahara, Sadao Kurohashi

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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 driv- ing, 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 experi- ments 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.

本文言語English
ホスト出版物のタイトルMRQA@EMNLP 2019 - Proceedings of the 2nd Workshop on Machine Reading for Question Answering
出版社Association for Computational Linguistics (ACL)
ページ98-104
ページ数7
ISBN(電子版)9781950737819
出版ステータスPublished - 2019
外部発表はい
イベント2nd Workshop on Machine Reading for Question Answering, MRQA@EMNLP 2019 - Hong Kong, China
継続期間: 2019 11月 4 → …

出版物シリーズ

名前MRQA@EMNLP 2019 - Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Conference

Conference2nd Workshop on Machine Reading for Question Answering, MRQA@EMNLP 2019
国/地域China
CityHong Kong
Period19/11/4 → …

ASJC Scopus subject areas

  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 人工知能

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