陈俊帆
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陈俊帆
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论文
Neural Dialogue State Tracking with Temporally Expressive Networks
发布时间：2025-10-22点击次数：
发表刊物：
Findings of the Association for Computational Linguistics: EMNLP 2020 (EMNLP Findings)
摘要：
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.
合写作者：
陈俊帆,张日崇, Yongyi Mao, Jie Xu
论文类型：
国际学术会议
页面范围：
1570--1579
是否译文：
否
发表时间：
2020-01-01