@inproceedings{xu-etal-2020-semantic,
title = "{S}emantic {R}ole {L}abeling {G}uided {M}ulti-turn {D}ialogue {R}e{W}riter",
author = "Xu, Kun and
Tan, Haochen and
Song, Linfeng and
Wu, Han and
Zhang, Haisong and
Song, Linqi and
Yu, Dong",
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.537",
doi = "10.18653/v1/2020.emnlp-main.537",
pages = "6632--6639",
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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<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.</abstract>
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%0 Conference Proceedings
%T Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
%A Xu, Kun
%A Tan, Haochen
%A Song, Linfeng
%A Wu, Han
%A Zhang, Haisong
%A Song, Linqi
%A Yu, Dong
%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 xu-etal-2020-semantic
%X 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.
%R 10.18653/v1/2020.emnlp-main.537
%U https://aclanthology.org/2020.emnlp-main.537
%U https://doi.org/10.18653/v1/2020.emnlp-main.537
%P 6632-6639
Markdown (Informal)
[Semantic Role Labeling Guided Multi-turn Dialogue ReWriter](https://aclanthology.org/2020.emnlp-main.537) (Xu et al., EMNLP 2020)
ACL
- Kun Xu, Haochen Tan, Linfeng Song, Han Wu, Haisong Zhang, Linqi Song, and Dong Yu. 2020. Semantic Role Labeling Guided Multi-turn Dialogue ReWriter. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6632–6639, Online. Association for Computational Linguistics.