In task-oriented dialogue systems, recent dialogue state tracking methods tend to perform one-pass generation of the dialogue state based on the previous dialogue state. The mistakes of these models made at the current turn are prone to be carried over to the next turn, causing error propagation. In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass. With the additional amending generation pass, our model is tasked to learn more robust dialogue state tracking by amending the errors that still exist in the primitive dialogue state, which plays the role of reviser in the double-checking process and alleviates unnecessary error propagation. Experimental results show that AG-DST significantly outperforms previous works in two active DST datasets (MultiWOZ 2.2 and WOZ 2.0), achieving new state-of-the-art performances.

本研究针对任务导向对话系统中对话状态跟踪方法的错误传播问题，提出了一种新的、可编辑生成的对话状态跟踪方法(AG-DST)，其中包含两次生成过程，通过额外的修订生成过程来修正原始对话状态中存在的错误。实验表明，AG-DST方法显著优于现有方法，在MultiWOZ 2.2和WOZ 2.0数据集上均取得了最新的性能。

可修改的对话状态跟踪生成