Active Learning Approaches to Enhancing Neural Machine Translation: An Empirical Study

Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper we explore new training frameworks by incorporating active learning into various techniques such as transfer learning and iterative back-translation (IBT) under a limited human translation budget. We design a word frequency based acquisition function and combine it with a strong uncertainty based method. The combined method steadily outperforms all other acquisition functions in various scenarios. As far as we know, we are the first to do a large-scale study on actively training Transformer (Vaswani et al., 2017) for NMT. Specifically, with a human translation budget of only 20% of the original parallel corpus, we manage to surpass Transformer trained on the entire parallel corpus in three language pairs.