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In recent years many multi-label classification methods have exploited label dependencies to improve performance of classification tasks in various domains, hence casting the tasks to structured prediction problems. We argue that multi-label predictions do not always satisfy domain constraint restrictions. For example when the dialogue state tracking task in task-oriented dialogue domains is solved with multi-label classification approaches, slot-value constraint rules should be enforced following real conversation scenarios.
To address these issues we propose an energy-based neural model to solve the dialogue state tracking task as a structured prediction problem. Furthermore we propose two improvements over previous methods with respect to dialogue slot-value constraint rules: (i) redefining the estimation conditions for the energy network; (ii) regularising label predictions following the dialogue slot-value constraint rules. In our results we find that our extended energy-based neural dialogue state tracker yields better overall performance in term of prediction accuracy, and also behaves more naturally with respect to the conversational rules.
Trinh A.D., Ross R.J., Kelleher J.D. (2020.) F-Measure Optimisation and Label Regularisation for Energy-Based Neural Dialogue State Tracking Models.The 29th International Conference on Artificial Neural Networks, ICANN 2020, Bratislava, Slovakia, September 15–18, 2020. doi:10.1007/978-3-030-61616-8_64