Domain-Adaptive Pretraining Methods for Dialogue Understanding

• URL: http://arxiv.org/abs/2105.13665v1
• Date: Fri, 28 May 2021 08:25:27 GMT
• Title: Domain-Adaptive Pretraining Methods for Dialogue Understanding
• Authors: Han Wu, Kun Xu, Linfeng Song, Lifeng Jin, Haisong Zhang, Linqi Song
• Abstract summary: 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.
• Score: 42.83187765297047
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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