Abstract
We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks for additional information using binary or multi-choice questions. At each turn, our system decides between asking the most informative question or making the final classification pre-diction. The simplicity of the model allows for bootstrapping of the system without interaction data, instead relying on simple crowd-sourcing tasks. We evaluate our approach on two domains, showing the benefit of interaction and the advantage of learning to balance between asking additional questions and making the final prediction.- Anthology ID:
- 2020.acl-main.237
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2664–2680
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.237
- DOI:
- 10.18653/v1/2020.acl-main.237
- Cite (ACL):
- Lili Yu, Howard Chen, Sida I. Wang, Tao Lei, and Yoav Artzi. 2020. Interactive Classification by Asking Informative Questions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2664–2680, Online. Association for Computational Linguistics.
- Cite (Informal):
- Interactive Classification by Asking Informative Questions (Yu et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.acl-main.237.pdf
- Code
- asappresearch/interactive-classification