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        <title>Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods</title>
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    <name type="personal">
        <namePart type="given">Ning</namePart>
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        <namePart type="given">Yuxuan</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <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-estimate 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.</abstract>
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%0 Conference Proceedings
%T Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods
%A Miao, Ning
%A Song, Yuxuan
%A Zhou, Hao
%A Li, Lei
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F miao-etal-2020-right
%X 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-estimate 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.
%R 10.18653/v1/2020.acl-main.314
%U https://aclanthology.org/2020.acl-main.314
%U https://doi.org/10.18653/v1/2020.acl-main.314
%P 3436-3441

Markdown (Informal)

[Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods](https://aclanthology.org/2020.acl-main.314) (Miao et al., ACL 2020)

• Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods (Miao et al., ACL 2020)

ACL

• Ning Miao, Yuxuan Song, Hao Zhou, and Lei Li. 2020. Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3436–3441, Online. Association for Computational Linguistics.