Incomplete Utterance Rewriting as Semantic Segmentation

Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-of-the-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Dialogue Rewriting Multi-Rewrite RUN+BERT Rewriting F3 47.7 # 1
Dialogue Rewriting Rewrite RUN+BERT ROUGE-L 93.5 # 1


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