11 citations · 29 across the 7 of their papers we have counts for
5 papers · 1 filter
CREDIT: Coarse-to-Fine Sequence Generation for Dialogue State Tracking
Zhi Chen, Lu Chen, Zihan Xu +3
In dialogue systems, a dialogue state tracker aims to accurately find a compact representation of the current dialogue status, based on the entire dialogue history. While previous…
Dual Learning for Dialogue State Tracking
Zhi Chen, Lu Chen, Yanbin Zhao +2
In task-oriented multi-turn dialogue systems, dialogue state refers to a compact representation of the user goal in the context of dialogue history. Dialogue state tracking (DST) i…
Structured Hierarchical Dialogue Policy with Graph Neural Networks
Zhi Chen, Xiaoyuan Liu, Lu Chen +1
Dialogue policy training for composite tasks, such as restaurant reservation in multiple places, is a practically important and challenging problem. Recently, hierarchical deep rei…
Deep Reinforcement Learning for On-line Dialogue State Tracking
Zhi Chen, Lu Chen, Xiang Zhou +1
Dialogue state tracking (DST) is a crucial module in dialogue management. It is usually cast as a supervised training problem, which is not convenient for on-line optimization. In…
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders
Yanbin Zhao, Lu Chen, Zhi Chen +1
Text simplification (TS) rephrases long sentences into simplified variants while preserving inherent semantics. Traditional sequence-to-sequence models heavily rely on the quantity…