Previous work on Incomplete Utterance Rewriting (IUR) has primarily focused on generating rewritten utterances based solely on dialogue context, ignoring the widespread phenomenon of coreference and ellipsis in dialogues. To address this issue, we propose a novel framework called TEO (\emph{Two-stage approach on Editing Operation}) for IUR, in which the first stage generates editing operations and the second stage rewrites incomplete utterances utilizing the generated editing operations and the dialogue context. Furthermore, an adversarial perturbation strategy is proposed to mitigate cascading errors and exposure bias caused by the inconsistency between training and inference in the second stage. Experimental results on three IUR datasets show that our TEO outperforms the SOTA models significantly.

翻译：先前关于不完整话语改写的研究主要集中于仅基于对话上下文生成改写后的话语，忽视了对话中普遍存在的指代与省略现象。为解决此问题，我们提出了一种名为TEO（基于编辑操作的两阶段方法）的新框架用于不完整话语改写。该框架第一阶段生成编辑操作，第二阶段利用生成的编辑操作与对话上下文对不完整话语进行改写。此外，我们提出了一种对抗扰动策略，以缓解第二阶段因训练与推理不一致导致的级联错误与曝光偏差。在三个不完整话语改写数据集上的实验结果表明，我们的TEO模型显著优于当前最优模型。