@inproceedings{wang-etal-2018-dynamic,
title = "Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation",
author = "Wang, Rui and
Utiyama, Masao and
Sumita, Eiichiro",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/fix-sig-urls/P18-2048/",
doi = "10.18653/v1/P18-2048",
pages = "298--304",
abstract = "Traditional Neural machine translation (NMT) involves a fixed training procedure where each sentence is sampled once during each epoch. In reality, some sentences are well-learned during the initial few epochs; however, using this approach, the well-learned sentences would continue to be trained along with those sentences that were not well learned for 10-30 epochs, which results in a wastage of time. Here, we propose an efficient method to dynamically sample the sentences in order to accelerate the NMT training. In this approach, a weight is assigned to each sentence based on the measured difference between the training costs of two iterations. Further, in each epoch, a certain percentage of sentences are dynamically sampled according to their weights. Empirical results based on the NIST Chinese-to-English and the WMT English-to-German tasks show that the proposed method can significantly accelerate the NMT training and improve the NMT performance."
}
Markdown (Informal)
[Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation](https://preview.aclanthology.org/fix-sig-urls/P18-2048/) (Wang et al., ACL 2018)
ACL