@inproceedings{wu-etal-2021-domain,
title = "Domain-Adaptive Pretraining Methods for Dialogue Understanding",
author = "Wu, Han and
Xu, Kun and
Song, Linfeng and
Jin, Lifeng and
Zhang, Haisong and
Song, Linqi",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.84",
doi = "10.18653/v1/2021.acl-short.84",
pages = "665--669",
abstract = "Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three objectives, including a novel objective focusing on modeling predicate-argument relations, are evaluated on two challenging dialogue understanding tasks. Experimental results demonstrate that domain-adaptive pretraining with proper objectives can significantly improve the performance of a strong baseline on these tasks, achieving the new state-of-the-art performances.",
}
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<abstract>Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three objectives, including a novel objective focusing on modeling predicate-argument relations, are evaluated on two challenging dialogue understanding tasks. Experimental results demonstrate that domain-adaptive pretraining with proper objectives can significantly improve the performance of a strong baseline on these tasks, achieving the new state-of-the-art performances.</abstract>
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%0 Conference Proceedings
%T Domain-Adaptive Pretraining Methods for Dialogue Understanding
%A Wu, Han
%A Xu, Kun
%A Song, Linfeng
%A Jin, Lifeng
%A Zhang, Haisong
%A Song, Linqi
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F wu-etal-2021-domain
%X Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three objectives, including a novel objective focusing on modeling predicate-argument relations, are evaluated on two challenging dialogue understanding tasks. Experimental results demonstrate that domain-adaptive pretraining with proper objectives can significantly improve the performance of a strong baseline on these tasks, achieving the new state-of-the-art performances.
%R 10.18653/v1/2021.acl-short.84
%U https://aclanthology.org/2021.acl-short.84
%U https://doi.org/10.18653/v1/2021.acl-short.84
%P 665-669
Markdown (Informal)
[Domain-Adaptive Pretraining Methods for Dialogue Understanding](https://aclanthology.org/2021.acl-short.84) (Wu et al., ACL-IJCNLP 2021)
ACL
- Han Wu, Kun Xu, Linfeng Song, Lifeng Jin, Haisong Zhang, and Linqi Song. 2021. Domain-Adaptive Pretraining Methods for Dialogue Understanding. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 665–669, Online. Association for Computational Linguistics.