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        <title>On Losses for Modern Language Models</title>
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        <namePart type="given">Stéphane</namePart>
        <namePart type="family">Aroca-Ouellette</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>BERT set many state-of-the-art results over varied NLU benchmarks by pre-training over two tasks: masked language modelling (MLM) and next sentence prediction (NSP), the latter of which has been highly criticized. In this paper, we 1) clarify NSP’s effect on BERT pre-training, 2) explore fourteen possible auxiliary pre-training tasks, of which seven are novel to modern language models, and 3) investigate different ways to include multiple tasks into pre-training. We show that NSP is detrimental to training due to its context splitting and shallow semantic signal. We also identify six auxiliary pre-training tasks – sentence ordering, adjacent sentence prediction, TF prediction, TF-IDF prediction, a FastSent variant, and a Quick Thoughts variant – that outperform a pure MLM baseline. Finally, we demonstrate that using multiple tasks in a multi-task pre-training framework provides better results than using any single auxiliary task. Using these methods, we outperform BERTBase on the GLUE benchmark using fewer than a quarter of the training tokens.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.403</identifier>
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%0 Conference Proceedings
%T On Losses for Modern Language Models
%A Aroca-Ouellette, Stéphane
%A Rudzicz, Frank
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F aroca-ouellette-rudzicz-2020-losses
%X BERT set many state-of-the-art results over varied NLU benchmarks by pre-training over two tasks: masked language modelling (MLM) and next sentence prediction (NSP), the latter of which has been highly criticized. In this paper, we 1) clarify NSP’s effect on BERT pre-training, 2) explore fourteen possible auxiliary pre-training tasks, of which seven are novel to modern language models, and 3) investigate different ways to include multiple tasks into pre-training. We show that NSP is detrimental to training due to its context splitting and shallow semantic signal. We also identify six auxiliary pre-training tasks – sentence ordering, adjacent sentence prediction, TF prediction, TF-IDF prediction, a FastSent variant, and a Quick Thoughts variant – that outperform a pure MLM baseline. Finally, we demonstrate that using multiple tasks in a multi-task pre-training framework provides better results than using any single auxiliary task. Using these methods, we outperform BERTBase on the GLUE benchmark using fewer than a quarter of the training tokens.
%R 10.18653/v1/2020.emnlp-main.403
%U https://aclanthology.org/2020.emnlp-main.403
%U https://doi.org/10.18653/v1/2020.emnlp-main.403
%P 4970-4981

Markdown (Informal)

[On Losses for Modern Language Models](https://aclanthology.org/2020.emnlp-main.403) (Aroca-Ouellette & Rudzicz, EMNLP 2020)

• On Losses for Modern Language Models (Aroca-Ouellette & Rudzicz, EMNLP 2020)

ACL

• Stéphane Aroca-Ouellette and Frank Rudzicz. 2020. On Losses for Modern Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4970–4981, Online. Association for Computational Linguistics.