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      Incomplete Utterance Rewriting as Semantic Segmentation

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          Abstract

          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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          Author and article information

          Journal
          28 September 2020
          Article
          2009.13166
          b6a99e8c-6572-4573-8252-a1d41e2ef6cf

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          To appear in EMNLP 2020 (Long Paper)
          cs.CL cs.LG

          Theoretical computer science,Artificial intelligence
          Theoretical computer science, Artificial intelligence

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