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        <title>Self-Paced Learning for Neural Machine Translation</title>
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        <namePart type="given">Yu</namePart>
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    <abstract>Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans. Nevertheless, achievements of such kind of curriculum learning rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity. We ameliorate this procedure with a more flexible manner by proposing self-paced learning, where NMT model is allowed to 1) automatically quantify the learning confidence over training examples; and 2) flexibly govern its learning via regulating the loss in each iteration step. Experimental results over multiple translation tasks demonstrate that the proposed model yields better performance than strong baselines and those models trained with human-designed curricula on both translation quality and convergence speed.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.80</identifier>
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        <date>2020-nov</date>
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            <start>1074</start>
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%0 Conference Proceedings
%T Self-Paced Learning for Neural Machine Translation
%A Wan, Yu
%A Yang, Baosong
%A Wong, Derek F.
%A Zhou, Yikai
%A Chao, Lidia S.
%A Zhang, Haibo
%A Chen, Boxing
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F wan-etal-2020-self
%X Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans. Nevertheless, achievements of such kind of curriculum learning rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity. We ameliorate this procedure with a more flexible manner by proposing self-paced learning, where NMT model is allowed to 1) automatically quantify the learning confidence over training examples; and 2) flexibly govern its learning via regulating the loss in each iteration step. Experimental results over multiple translation tasks demonstrate that the proposed model yields better performance than strong baselines and those models trained with human-designed curricula on both translation quality and convergence speed.
%R 10.18653/v1/2020.emnlp-main.80
%U https://aclanthology.org/2020.emnlp-main.80
%U https://doi.org/10.18653/v1/2020.emnlp-main.80
%P 1074-1080

Markdown (Informal)

[Self-Paced Learning for Neural Machine Translation](https://aclanthology.org/2020.emnlp-main.80) (Wan et al., EMNLP 2020)

• Self-Paced Learning for Neural Machine Translation (Wan et al., EMNLP 2020)

ACL

• Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, and Boxing Chen. 2020. Self-Paced Learning for Neural Machine Translation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1074–1080, Online. Association for Computational Linguistics.