Incomplete Utterance Rewriting as Semantic Segmentation

Qian Liu, Bei Chen, Jian-Guang Lou, Bin Zhou, Dongmei Zhang


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.
Anthology ID:
2020.emnlp-main.227
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2846–2857
Language:
URL:
https://aclanthology.org/2020.emnlp-main.227
DOI:
10.18653/v1/2020.emnlp-main.227
Bibkey:
Cite (ACL):
Qian Liu, Bei Chen, Jian-Guang Lou, Bin Zhou, and Dongmei Zhang. 2020. Incomplete Utterance Rewriting as Semantic Segmentation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2846–2857, Online. Association for Computational Linguistics.
Cite (Informal):
Incomplete Utterance Rewriting as Semantic Segmentation (Liu et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2020.emnlp-main.227.pdf
Video:
 https://slideslive.com/38938666
Code
 microsoft/ContextualSP
Data
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