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Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture thesource of important words, which is crucial to edit the incomplete utterance,and introduce words from irrelevant utterances. We propose a novel andeffective multi-task information interaction framework including contextselection, edit matrix construction, and relevance merging to capture themulti-granularity of semantic information. Benefiting from fetching therelevant utterance and figuring out the important words, our approachoutperforms existing state-of-the-art models on two benchmark datasetsRestoration-200K and CANAND in this field. Code will be provided on\url{https://github.com/yanmenxue/QR}.

Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting