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Domain-Adaptive Pretraining Methods for Dialogue Understanding

2021-05-28 08:25:27
Han Wu, Kun Xu, Linfeng Song, Lifeng Jin, Haisong Zhang, Linqi Song
arXiv_AI
           
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 (translated)

URL

https://arxiv.org/abs/2105.13665

PDF

https://arxiv.org/pdf/2105.13665.pdf

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