Minimum Risk Training for Neural Machine Translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei Ping He, Hua Ren Wu, Maosong Sun, Yang Liu · 2016
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.