Abstract
Sequence-to-sequence models are a common approach to develop a chatbot. They can train a conversational model in an end-to-end manner. One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear. To tackle this problem, an interpretable response generation mechanism is desired. As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process. In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs. Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8% higher F1-score and 3.0% higher accuracy on DA prediction.- Anthology ID:
- P19-2027
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 197–202
- Language:
- URL:
- https://aclanthology.org/P19-2027
- DOI:
- 10.18653/v1/P19-2027
- Cite (ACL):
- Koji Tanaka, Junya Takayama, and Yuki Arase. 2019. Dialogue-Act Prediction of Future Responses Based on Conversation History. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 197–202, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Dialogue-Act Prediction of Future Responses Based on Conversation History (Tanaka et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/P19-2027.pdf