End-to-End Task-Completion Neural Dialogue Systems
Journal
8th International Joint Conference on Natural Language Processing - Proceedings of the IJCNLP 2017, System Demonstrations
Journal Volume
1
Start Page
733
End Page
743
ISBN (of the container)
978-194808702-5
ISBN
[9781948087025]
Date Issued
2017
Author(s)
Abstract
One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module
Event(s)
8th International Joint Conference on Natural Language Processing, IJCNLP 2017
Publisher
Association for Computational Linguistics (ACL)
Type
conference paper