Key-Value Retrieval Networks for Task-Oriented Dialogue
Mihail Eric, Lakshmi Krishnan, Francois Charette, Christopher D. Manning
Abstract
Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base. In this work, we seek to address this problem by proposing a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism. The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. We also release a new dataset of 3,031 dialogues that are grounded through underlying knowledge bases and span three distinct tasks in the in-car personal assistant space: calendar scheduling, weather information retrieval, and point-of-interest navigation. Our architecture is simultaneously trained on data from all domains and significantly outperforms a competitive rule-based system and other existing neural dialogue architectures on the provided domains according to both automatic and human evaluation metrics.- Anthology ID:
- W17-5506
- Volume:
- Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- August
- Year:
- 2017
- Address:
- Saarbrücken, Germany
- Editors:
- Kristiina Jokinen, Manfred Stede, David DeVault, Annie Louis
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 37–49
- Language:
- URL:
- https://aclanthology.org/W17-5506
- DOI:
- 10.18653/v1/W17-5506
- Cite (ACL):
- Mihail Eric, Lakshmi Krishnan, Francois Charette, and Christopher D. Manning. 2017. Key-Value Retrieval Networks for Task-Oriented Dialogue. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 37–49, Saarbrücken, Germany. Association for Computational Linguistics.
- Cite (Informal):
- Key-Value Retrieval Networks for Task-Oriented Dialogue (Eric et al., SIGDIAL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W17-5506.pdf
- Code
- additional community code