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Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods

2020-07-13 02:53:03
Ning Miao, Yuxuan Song, Hao Zhou, Lei Li
arXiv_CL
       
Abstract

It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data. In practice, we observe that fine-tuning a pre-trained model on a small dataset may lead to over- and/or under-estimation problem. In this paper, we propose MC-Tailor, a novel method to alleviate the above issue in text generation tasks by truncating and transferring the probability mass from over-estimated regions to under-estimated ones. Experiments on a variety of text generation datasets show that MC-Tailor consistently and significantly outperforms the fine-tuning approach. Our code is available at this url.

Abstract (translated)

URL

https://arxiv.org/abs/2007.06162

PDF

https://arxiv.org/pdf/2007.06162.pdf

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