Abstract
Machine reading comprehension (MRC) is a task for question answering that finds answers to questions from documents of knowledge. Most studies on the domain adaptation of MRC require documents describing knowledge of the target domain. However, it is sometimes difficult to prepare such documents. The goal of this study was to transfer an MRC model to another domain without documents in an unsupervised manner. Therefore, unlike previous studies, we propose a domain-adaptation framework of MRC under the assumption that the only available data in the target domain are human conversations between a user asking questions and an expert answering the questions. The framework consists of three processes: (1) training an MRC model on the source domain, (2) converting conversations into documents using document generation (DG), a task we developed for retrieving important information from several human conversations and converting it to an abstractive document text, and (3) transferring the MRC model to the target domain with unsupervised domain adaptation. To the best of our knowledge, our research is the first to use conversation data to train MRC models in an unsupervised manner. We show that the MRC model successfully obtains question-answering ability from conversations in the target domain.- Anthology ID:
- 2022.sigdial-1.42
- Volume:
- Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- September
- Year:
- 2022
- Address:
- Edinburgh, UK
- Editors:
- Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 432–441
- Language:
- URL:
- https://aclanthology.org/2022.sigdial-1.42
- DOI:
- 10.18653/v1/2022.sigdial-1.42
- Cite (ACL):
- Amalia Adiba, Takeshi Homma, and Yasuhiro Sogawa. 2022. Unsupervised Domain Adaptation on Question-Answering System with Conversation Data. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 432–441, Edinburgh, UK. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Domain Adaptation on Question-Answering System with Conversation Data (Adiba et al., SIGDIAL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.sigdial-1.42.pdf
- Data
- CNN/Daily Mail, Doc2Dial, doc2dial