MULTI-TURN INCOMPLETE UTTERANCE RESTORATION AS OBJECT DETECTION

In this paper, we investigate the task of multi-turn incomplete utterance restoration to tackle the issue of frequent coreference and information omission in multi-turn dialogues. Recent works mainly focus on edit-based approaches which have been proven to outperform traditional generation-based models in terms of accuracy and efficiency. However, they only model token-level edit relationships while ignoring span-level edit relationships. Our experiments find this breaks the semantic integrity of edit span, which causes inaccurate edit span prediction and disfluent utterance restoration. To address the problem, we propose a novel approach to directly model span-level edit relationships between the incomplete utterance and context. Specifically, we build an edit matrix in which each rectangular region represents a span-level edit operation. Then, we detect the region with a well-designed dual-branch detection module inspired by object detection. Empirical results demonstrate that our method outperforms state-of-the-art methods significantly on two public datasets. In addition, further studies verify that our method is capable of preserving the semantic integrity of edit span.