Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
Shuke Peng, Xinjing Huang, Zehao Lin, Feng Ji, Haiqing Chen, Yin, Zhang

TL;DR
This paper introduces a teacher-student framework for multi-domain dialogue generation that eliminates the need for external state trackers, leveraging domain-specific teachers to train a universal student model directly on raw utterances.
Contribution
The novel framework trains a universal dialogue model without external state trackers by transferring knowledge from domain-specific teacher models to a student model using raw utterances.
Findings
Outperforms belief tracker-based systems in experiments
Effectively merges domain-specific knowledge into a universal model
Reduces error propagation in dialogue state tracking
Abstract
Dialogue systems dealing with multi-domain tasks are highly required. How to record the state remains a key problem in a task-oriented dialogue system. Normally we use human-defined features as dialogue states and apply a state tracker to extract these features. However, the performance of such a system is limited by the error propagation of a state tracker. In this paper, we propose a dialogue generation model that needs no external state trackers and still benefits from human-labeled semantic data. By using a teacher-student framework, several teacher models are firstly trained in their individual domains, learn dialogue policies from labeled states. And then the learned knowledge and experience are merged and transferred to a universal student model, which takes raw utterance as its input. Experiments show that the dialogue system trained under our framework outperforms the one uses…
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Taxonomy
TopicsSpeech and dialogue systems · Topic Modeling · Natural Language Processing Techniques
