@inproceedings{liu-etal-2020-incomplete,
title = "Incomplete Utterance Rewriting as Semantic Segmentation",
author = "Liu, Qian and
Chen, Bei and
Lou, Jian-Guang and
Zhou, Bin and
Zhang, Dongmei",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.227",
doi = "10.18653/v1/2020.emnlp-main.227",
pages = "2846--2857",
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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<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.</abstract>
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%0 Conference Proceedings
%T Incomplete Utterance Rewriting as Semantic Segmentation
%A Liu, Qian
%A Chen, Bei
%A Lou, Jian-Guang
%A Zhou, Bin
%A Zhang, Dongmei
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-incomplete
%X 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.
%R 10.18653/v1/2020.emnlp-main.227
%U https://aclanthology.org/2020.emnlp-main.227
%U https://doi.org/10.18653/v1/2020.emnlp-main.227
%P 2846-2857
Markdown (Informal)
[Incomplete Utterance Rewriting as Semantic Segmentation](https://aclanthology.org/2020.emnlp-main.227) (Liu et al., EMNLP 2020)
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.