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    <titleInfo>
        <title>Learning Better Masking for Better Language Model Pre-training</title>
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    <name type="personal">
        <namePart type="given">Dongjie</namePart>
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        <namePart type="given">Zhuosheng</namePart>
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        <dateIssued>2023-07</dateIssued>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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    <abstract>Masked Language Modeling (MLM) has been widely used as the denoising objective in pre-training language models (PrLMs). Existing PrLMs commonly adopt a Random-Token Masking strategy where a fixed masking ratio is applied and different contents are masked by an equal probability throughout the entire training. However, the model may receive complicated impact from pre-training status, which changes accordingly as training time goes on. In this paper, we show that such time-invariant MLM settings on masking ratio and masked content are unlikely to deliver an optimal outcome, which motivates us to explore the influence of time-variant MLM settings. We propose two scheduled masking approaches that adaptively tune the masking ratio and masked content in different training stages, which improves the pre-training efficiency and effectiveness verified on the downstream tasks. Our work is a pioneer study on time-variant masking strategy on ratio and content and gives a better understanding of how masking ratio and masked content influence the MLM pre-training.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-long.400</identifier>
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        <date>2023-07</date>
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%0 Conference Proceedings
%T Learning Better Masking for Better Language Model Pre-training
%A Yang, Dongjie
%A Zhang, Zhuosheng
%A Zhao, Hai
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yang-etal-2023-learning
%X Masked Language Modeling (MLM) has been widely used as the denoising objective in pre-training language models (PrLMs). Existing PrLMs commonly adopt a Random-Token Masking strategy where a fixed masking ratio is applied and different contents are masked by an equal probability throughout the entire training. However, the model may receive complicated impact from pre-training status, which changes accordingly as training time goes on. In this paper, we show that such time-invariant MLM settings on masking ratio and masked content are unlikely to deliver an optimal outcome, which motivates us to explore the influence of time-variant MLM settings. We propose two scheduled masking approaches that adaptively tune the masking ratio and masked content in different training stages, which improves the pre-training efficiency and effectiveness verified on the downstream tasks. Our work is a pioneer study on time-variant masking strategy on ratio and content and gives a better understanding of how masking ratio and masked content influence the MLM pre-training.
%R 10.18653/v1/2023.acl-long.400
%U https://aclanthology.org/2023.acl-long.400/
%U https://doi.org/10.18653/v1/2023.acl-long.400
%P 7255-7267

Markdown (Informal)

[Learning Better Masking for Better Language Model Pre-training](https://aclanthology.org/2023.acl-long.400/) (Yang et al., ACL 2023)

• Learning Better Masking for Better Language Model Pre-training (Yang et al., ACL 2023)

ACL

• Dongjie Yang, Zhuosheng Zhang, and Hai Zhao. 2023. Learning Better Masking for Better Language Model Pre-training. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7255–7267, Toronto, Canada. Association for Computational Linguistics.