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Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked. Furthermore, dialog state tracking models do not yet explicitly make use of relationships between dialogue variables, such as slots across domains. We propose using energy-based structure prediction methods for large-scale dialogue state tracking task in two multiple domain dialogue datasets. Our results indicate that: (i) modelling variable dependencies yields better results; and (ii) the structured prediction output aligns with the dialogue slot-value constraint principles. This leads to promising directions to improve state-of-the-art models by incorporating variable dependencies into their prediction process.
Trinh A.D., Ross R.J., Kelleher J.D. (2020) Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking. The 4th Workshop on Structured Prediction for NLP, SPNLP 2020, November 20, 2020. doi:10.18653/v1/W19-4109