Dialogue state tracking for low-resource languages

Abstract

Despite ground breaking work in the academia, current state-of-the-art work in goal-oriented conversational-agents has not been able to fulfil the demand of the industry for multi-domain multi-lingual, adaptable, dialogue systems. Data-intensive nature of deep learning models used in dialogue state tracking (DST) module, which is a core component of the goal-oriented dialogue architecture, and the lack of large labelled dialogue corpus for state tracking are two main factors which have hindered the progress. We identified, modeling with separate natural language understanding (NLU) module and joint modeling of dialogue state tracker with NLU as the two main approaches for state tracking, and accordingly made two major contributions. First, we propose a novel meta-learning algorithm for intent detection and slot-filling tasks, focusing on models with separate NLU. Our work empirically demonstrates that the proposed meta-learning approach is capable of learning a meta-parameter(prior) from similar, but different tasks. Compared to the random initialization, which regular supervised learning algorithms rely on, proposed method significantly improves the accuracy in both intent detection and slot-filling tasks in few-shot (5-way 1-shot and 5-way 2-shot) settings. Further, our effective use of meta-learning for intent detection and slot-filling opens up new line of research for DST. Second, we systematically review the progression of joint NLU/DST models with special emphasis on their ability to generalize and adapt to new domains and languages.