International Conference on Computer and Knowledge Engineering

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Span-prediction of Unknown Values for Long-sequence Dialogue State Tracking

Authors :

Marzieh Naghdi Dorabati¹ Reza Ramezani² Mohammad Ali Nematbakhsh³

1- Department of Computer Engineering, University of Isfahan 2- Dept. of Computer Engineering, University of Isfahan 3- Dept. of Computer Engineering, University of Isfahan

Keywords :

Task-oriented Dialogue Systems،Dialogue State Tracking،Unknown Values،Diversity of User Utterances،Long Sequences

Abstract :

Abstract—Dialogue state tracking is one of the main components in task-oriented dialogue systems whose duty is tracking the user goal during the conversation. Due to the diversity in natural languages and existing different utterances, the user requests may include unknown values at different turns in these systems. However, predicting true values of the user requests is necessary for completing the intended task. In existing studies, these values are determined using span-based methods to predict a span in utterances or previous dialogues. However, the slots are not correctly filled when values are multi-word. In addition, in some scenarios, the slot values in a given turn may depend on previously dialogue states. However, due to the limitation of the input length of language models, it is impossible to access all the previous dialogue states. In this study, a new approach is proposed that uses a span-tokenizer and adds the Bi-LSTM layer on top of the BERT model to predict the exact span of multi-word values. This approach uses parameters like user utterance, important dialogue histories, and all dialogue states as input to decrease the length of the sequences. The results show that this strategy has led to 1.80% improvement in the joint-goal accuracy and 0.15% improvement in the slot accuracy metrics over the MultiWOZ 2.1 dataset compared to the SAVN model.