Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
Abstract
We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. First, three language modeling tasks are used to pre-train the transformers, token- and utterance-level language modeling and utterance order prediction, that learn both token and utterance embeddings for better understanding in dialogue contexts. Then, multi-task learning between the utterance prediction and the token span prediction is applied to fine-tune for span-based question answering (QA). Our approach is evaluated on the FriendsQA dataset and shows improvements of 3.8% and 1.4% over the two state-of-the-art transformer models, BERT and RoBERTa, respectively.- Anthology ID:
- 2020.acl-main.505
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5709–5714
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.505
- DOI:
- 10.18653/v1/2020.acl-main.505
- Cite (ACL):
- Changmao Li and Jinho D. Choi. 2020. Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5709–5714, Online. Association for Computational Linguistics.
- Cite (Informal):
- Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering (Li & Choi, ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.505.pdf