T-Mask: An Active and Accurate Dialogue State Tracking with Token Mask Prediction

Shezheng Song, Changjian Wang, Dongsong Zhang, Zhen Huang, Yuxing Peng

Abstract: Recent dialogue state tracking (DST) usually treats utterance, system action and ontology equally to estimate the slot types and values. In this way, the expression of slot in utterance is restricted. As the main way to directly express user semantics, utterance should receive further attention and its proportion in semantic expression should be dynamic according to the content. It’s common to recognize the different importance of information in all the DST models. However, most of them pay little attention to the position of slot in utterance. In fact, position and semantics are related due to human grammatical habits and expression habits. Therefore, we propose T-Mask <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a model to actively and accurately learn the token mask position of slot, and we further utilize the learned position information to influence the semantic expression of utterance. We verify the effectiveness of our model on DSTC2 and WoZ2.0. On WoZ2.0, we achieve 90.84 joint goal accuracy and 97.6 turn request accuracy, which is better than most existing models.