@inproceedings{xu-etal-2020-dynamic,
title = "Dynamic Curriculum Learning for Low-Resource Neural Machine Translation",
author = "Xu, Chen and
Hu, Bojie and
Jiang, Yufan and
Feng, Kai and
Wang, Zeyang and
Huang, Shen and
Ju, Qi and
Xiao, Tong and
Zhu, Jingbo",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.352",
doi = "10.18653/v1/2020.coling-main.352",
pages = "3977--3989",
abstract = "Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training. Unlike previous work, we do not use a static scoring function for reordering. Instead, the order of training samples is dynamically determined in two ways - loss decline and model competence. This eases training by highlighting easy samples that the current model has enough competence to learn. We test our DCL method in a Transformer-based system. Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT{'}16 En-De.",
}
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<abstract>Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training. Unlike previous work, we do not use a static scoring function for reordering. Instead, the order of training samples is dynamically determined in two ways - loss decline and model competence. This eases training by highlighting easy samples that the current model has enough competence to learn. We test our DCL method in a Transformer-based system. Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De.</abstract>
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%0 Conference Proceedings
%T Dynamic Curriculum Learning for Low-Resource Neural Machine Translation
%A Xu, Chen
%A Hu, Bojie
%A Jiang, Yufan
%A Feng, Kai
%A Wang, Zeyang
%A Huang, Shen
%A Ju, Qi
%A Xiao, Tong
%A Zhu, Jingbo
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F xu-etal-2020-dynamic
%X Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training. Unlike previous work, we do not use a static scoring function for reordering. Instead, the order of training samples is dynamically determined in two ways - loss decline and model competence. This eases training by highlighting easy samples that the current model has enough competence to learn. We test our DCL method in a Transformer-based system. Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De.
%R 10.18653/v1/2020.coling-main.352
%U https://aclanthology.org/2020.coling-main.352
%U https://doi.org/10.18653/v1/2020.coling-main.352
%P 3977-3989
Markdown (Informal)
[Dynamic Curriculum Learning for Low-Resource Neural Machine Translation](https://aclanthology.org/2020.coling-main.352) (Xu et al., COLING 2020)
ACL
- Chen Xu, Bojie Hu, Yufan Jiang, Kai Feng, Zeyang Wang, Shen Huang, Qi Ju, Tong Xiao, and Jingbo Zhu. 2020. Dynamic Curriculum Learning for Low-Resource Neural Machine Translation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3977–3989, Barcelona, Spain (Online). International Committee on Computational Linguistics.