We propose minimum risk training for end-to-end neural machine translation. Unlike conventional maximum likelihood estimation, minimum risk training is capable of optimizing model parameters directly with respect to arbitrary evaluation metrics, which are not necessarily differentiable. Experiments show that our approach achieves significant improvements over maximum likelihood estimation on a state-of-the-art neural machine translation system across various languages pairs. Transparent to architectures, our approach can be applied to more neural networks and potentially benefit more NLP tasks.
REF introduced the Minimum Risk Training (MRT) into the end-to-end NMT model, which optimized model parameters by minimizing directly the expected loss with respect to arbitrary evaluation metrics, e.g., sentence-level BLEU.
Minimum Risk Training for Neural Machine Translation
ArXiv
ArXiv
Computer Science