%0 Conference Proceedings %T Improving Back-Translation with Uncertainty-based Confidence Estimation %A Wang, Shuo %A Liu, Yang %A Wang, Chao %A Luan, Huanbo %A Sun, Maosong %Y Inui, Kentaro %Y Jiang, Jing %Y Ng, Vincent %Y Wan, Xiaojun %S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) %D 2019 %8 November %I Association for Computational Linguistics %C Hong Kong, China %F wang-etal-2019-improving-back %X While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy. In this work, we propose to quantify the confidence of NMT model predictions based on model uncertainty. With word- and sentence-level confidence measures based on uncertainty, it is possible for back-translation to better cope with noise in synthetic bilingual corpora. Experiments on Chinese-English and English-German translation tasks show that uncertainty-based confidence estimation significantly improves the performance of back-translation. %R 10.18653/v1/D19-1073 %U https://aclanthology.org/D19-1073 %U https://doi.org/10.18653/v1/D19-1073 %P 791-802