Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks
Janarthanan Rajendran, Jonathan K. Kummerfeld, Satinder Baveja
Abstract
For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system. Collecting that data is a costly and time-consuming process. Instead, we show that we can use only a small amount of data, supplemented with data from a related dialog task. Naively learning from related data fails to improve performance as the related data can be inconsistent with the target task. We describe a meta-learning based method that selectively learns from the related dialog task data. Our approach leads to significant accuracy improvements in an example dialog task.- Anthology ID:
- 2021.nlp4convai-1.16
- Volume:
- Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Venue:
- NLP4ConvAI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 163–178
- Language:
- URL:
- https://aclanthology.org/2021.nlp4convai-1.16
- DOI:
- 10.18653/v1/2021.nlp4convai-1.16
- Cite (ACL):
- Janarthanan Rajendran, Jonathan K. Kummerfeld, and Satinder Baveja. 2021. Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, pages 163–178, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks (Rajendran et al., NLP4ConvAI 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.nlp4convai-1.16.pdf