Semantic Role Labeling Guided Multi-turn Dialogue ReWriter

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Kun Xu
  • Linfeng Song
  • Haisong Zhang
  • Dong Yu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
EditorsBonnie Webber, Trevor Cohn, Yulan He, Yang Liu
PublisherAssociation for Computational Linguistics
Pages6632–6639
Publication statusPublished - Nov 2020

Conference

TitleThe 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)
LocationVirtual
Period16 - 20 November 2020

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Abstract

For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting ride of the noises is essential to improve its performance. Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words. In this paper, we propose to use semantic role labeling (SRL), which highlights the core semantic information of who did what to whom, to provide additional guidance for the rewriter model. Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous state-of-the-art systems.

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Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Semantic Role Labeling Guided Multi-turn Dialogue ReWriter. / Xu, Kun; Tan, Haochen; Song, Linfeng; Wu, Han; Zhang, Haisong; Song, Linqi; Yu, Dong.

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). ed. / Bonnie Webber; Trevor Cohn; Yulan He; Yang Liu. Association for Computational Linguistics, 2020. p. 6632–6639.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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