Incomplete Utterance Rewriting as Semantic Segmentation
Qian Liu, Bei Chen, Jian–Guang Lou, Bin Zhou, Dongmei Zhang · 2020
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-ofthe-art performance on several public datasets.Furthermore, our approach is four times faster than the standard approach in inference.