Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting (2023)

Abstract: Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, which is crucial to edit the incomplete utterance, and introduce words from irrelevant utterances. We propose a novel and effective multi-task information interaction framework including context selection, edit matrix construction, and relevance merging to capture the multi-granularity of semantic information. Benefiting from fetching the relevant utterance and figuring out the important words, our approach outperforms existing state-of-the-art models on two benchmark datasets Restoration-200K and CANAND in this field.

## introduction recently increasing attention has been paid to multi-turn dialogue modeling @xcite @xcite and the major challenge in this field is that speakers tend to use incomplete utterances for brevity, such as referring back to (i.e., co-reference) or omitting (i.e., ellipsis) entities or concepts that appear in dialogue history. @xcite shows that ellipsis and co-reference can exist in more than 70% of dialogue utterances. to handle this, the task of incomplete utterance rewriting (iur) @xcite is proposed to rewrite an incomplete utterance into an utterance which is semantically equivalent but self-contained to be understood without context. to maintain the similarity of semantic structure between the incomplete utterance and rewritten utterance, recent approaches formulate it as a word edit task @xcite and predict the edit types by capturing the semantic relations between words. however, the sentence-level semantic relations between contextual utterances and incomplete utterance are ne- ## related work there are two main streams of approaches to tackle the task of iur: generation-based @xcite and edit-based @xcite . generation-based models solve this task as a seq2seq problem. @xcite utilize pointer network to respectively predict the prob of tokens in rewritten utterance from contexts and incomplete utterance. @xcite formulate the task as sequence tagging to reduce the search space. @xcite combine a source sequence tagger with an lstm-based decoder to maintain the grammatical correctness. generation models lack to capture the trait of iur, where the main semantic structure of a rewritten utterance is usually similar to the original incomplete utterance. edit-based models focus on predicting wordlevel or span-level edit type between contextual utterances and the incomplete utterance. @xcite formulate this task as semantic segmentation and propose u-net @xcite to encode the word-level semantic relations. to incorporate the rich information in self-attention weights of pretrained language model (ptm) @xcite al., 2018), zhang et al. (2022) directly take the self-attention weight matrix as the word2word edit matrix. though these models produce competitive performance, the sentence-level semantic relations are neglected and the models tend to introduce incorrect words from irrelevant contextual utterances. ## conclusion in this paper, we argue that capturing the source of important words can diminish to introduce irrelevant words for iur. we propose a novel and effective multi-task framework including context selection, edit matrix construction, and relevance merging to capture the multi-granularity of semantic information and fetch the relevant utterance. we do experiments on two benchmark datasets in iur and show competitive performance.