Abstract
Argumentative dialogue is an important process where speakers discuss a specific theme for consensus building or decision making. In previous studies for generating consistent argumentative dialogue, retrieval-based methods with hand-crafted argumentation structures have been used. In this study, we propose a method to generate natural argumentative dialogues by combining an argumentation structure and language model. We trained the language model to rewrite a proposition of an argumentation structure on the basis of its information, such as keywords and stance, into the next utterance while considering its context, and we used the model to rewrite propositions in the argumentation structure. We manually evaluated the generated dialogues and found that the proposed method significantly improved the naturalness of dialogues without losing consistency of argumentation.- Anthology ID:
- 2022.aacl-short.9
- Volume:
- Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Venues:
- AACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 65–71
- Language:
- URL:
- https://aclanthology.org/2022.aacl-short.9
- DOI:
- Cite (ACL):
- Koh Mitsuda, Ryuichiro Higashinaka, and Kuniko Saito. 2022. Combining Argumentation Structure and Language Model for Generating Natural Argumentative Dialogue. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 65–71, Online only. Association for Computational Linguistics.
- Cite (Informal):
- Combining Argumentation Structure and Language Model for Generating Natural Argumentative Dialogue (Mitsuda et al., AACL-IJCNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.aacl-short.9.pdf