AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval

Chieh-Yang Chen, Pei-Hsin Wang, Shih-Chieh Chang, Da-Cheng Juan, Wei Wei, Jia-Yu Pan


Abstract
Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms. To alleviate the above problem, we propose , an end-to-end trainable text-to-SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries. Specifically, the neural agent first learns to ask and confirm the customer’s intent during the multi-turn interactions, then dynamically determining when to ground the user constraints into executable SQL queries so as to fetch relevant information from KBs. With the help of our method, the agent can use less but more accurate fetched results to generate useful responses efficiently, instead of incorporating the entire KBs. We evaluate the proposed method on the AirDialogue dataset, a large corpus released by Google, containing the conversations of customers booking flight tickets from the agent. The experimental results show that significantly improves over previous work in terms of accuracy and the BLEU score, which demonstrates not only the ability to achieve the given task but also the good quality of the generated dialogues.
Anthology ID:
2020.findings-emnlp.79
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
884–897
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.79
DOI:
10.18653/v1/2020.findings-emnlp.79
Bibkey:
Cite (ACL):
Chieh-Yang Chen, Pei-Hsin Wang, Shih-Chieh Chang, Da-Cheng Juan, Wei Wei, and Jia-Yu Pan. 2020. AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 884–897, Online. Association for Computational Linguistics.
Cite (Informal):
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval (Chen et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.79.pdf
Optional supplementary material:
 2020.findings-emnlp.79.OptionalSupplementaryMaterial.zip
Code
 Leftice/AirConcierge