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        <title>Instance Regularization for Discriminative Language Model Pre-training</title>
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        <namePart type="given">Zhuosheng</namePart>
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    <abstract>Discriminative pre-trained language models (PrLMs) can be generalized as denoising auto-encoders that work with two procedures, ennoising and denoising. First, an ennoising process corrupts texts with arbitrary noising functions to construct training instances. Then, a denoising language model is trained to restore the corrupted tokens. Existing studies have made progress by optimizing independent strategies of either ennoising or denosing. They treat training instances equally throughout the training process, with little attention on the individual contribution of those instances. To model explicit signals of instance contribution, this work proposes to estimate the complexity of restoring the original sentences from corrupted ones in language model pre-training. The estimations involve the corruption degree in the ennoising data construction process and the prediction confidence in the denoising counterpart. Experimental results on natural language understanding and reading comprehension benchmarks show that our approach improves pre-training efficiency, effectiveness, and robustness. Code is publicly available at https://github.com/cooelf/InstanceReg.</abstract>
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%0 Conference Proceedings
%T Instance Regularization for Discriminative Language Model Pre-training
%A Zhang, Zhuosheng
%A Zhao, Hai
%A Zhou, Ming
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhang-etal-2022-instance
%X Discriminative pre-trained language models (PrLMs) can be generalized as denoising auto-encoders that work with two procedures, ennoising and denoising. First, an ennoising process corrupts texts with arbitrary noising functions to construct training instances. Then, a denoising language model is trained to restore the corrupted tokens. Existing studies have made progress by optimizing independent strategies of either ennoising or denosing. They treat training instances equally throughout the training process, with little attention on the individual contribution of those instances. To model explicit signals of instance contribution, this work proposes to estimate the complexity of restoring the original sentences from corrupted ones in language model pre-training. The estimations involve the corruption degree in the ennoising data construction process and the prediction confidence in the denoising counterpart. Experimental results on natural language understanding and reading comprehension benchmarks show that our approach improves pre-training efficiency, effectiveness, and robustness. Code is publicly available at https://github.com/cooelf/InstanceReg.
%R 10.18653/v1/2022.emnlp-main.773
%U https://aclanthology.org/2022.emnlp-main.773
%U https://doi.org/10.18653/v1/2022.emnlp-main.773
%P 11255-11265

Markdown (Informal)

[Instance Regularization for Discriminative Language Model Pre-training](https://aclanthology.org/2022.emnlp-main.773) (Zhang et al., EMNLP 2022)

• Instance Regularization for Discriminative Language Model Pre-training (Zhang et al., EMNLP 2022)

ACL

• Zhuosheng Zhang, Hai Zhao, and Ming Zhou. 2022. Instance Regularization for Discriminative Language Model Pre-training. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11255–11265, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.