Dynamic Curriculum Learning for Low-Resource Neural Machine Translation

• URL: http://arxiv.org/abs/2011.14608v1
• Date: Mon, 30 Nov 2020 08:13:41 GMT
• Title: Dynamic Curriculum Learning for Low-Resource Neural Machine Translation
• Authors: Chen Xu, Bojie Hu, Yufan Jiang, Kai Feng, Zeyang Wang, Shen Huang, Qi Ju, Tong Xiao, Jingbo Zhu
• Abstract summary: 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. This eases training by highlighting easy samples that the current model has enough competence to learn.
• Score: 27.993407441922507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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