Journal:Findings of the Association for Computational Linguistics: EMNLP 2020 (EMNLP Findings)
Abstract:Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation.
Co-author:Junfan Chen,Richong Zhang, Yongyi Mao, Jie Xu
Indexed by:国际学术会议
Page Number:1570--1579
Translation or Not:no
Date of Publication:2020-01-01