Although Neural Machine Translation (NMT) models have advanced state-of-the-art performance in machine translation, they face problems like the inadequate translation. We attribute this to that the standard Maximum Likelihood Estimation (MLE) cannot judge the real translation quality due to its several limitations. In this work, we propose an adequacy-oriented learning mechanism for NMT by casting translation as a stochastic policy in Reinforcement Learning (RL), where the reward is estimated by explicitly measuring translation adequacy. Benefiting from the sequence-level training of RL strategy and a more accurate reward designed specifically for translation, our model outperforms multiple strong baselines, including (1) standard and coverage-augmented attention models with MLE-based training, and (2) advanced reinforcement and adversarial training strategies with rewards based on both word-level BLEU and character-level chrF3. Quantitative and qualitative analyses on different language pairs and NMT architectures demonstrate the effectiveness and universality of the proposed approach.
Inadequate translation problem is a commonly-cited weakness of NMT models @cite_16 . A number of recent efforts have explored ways to alleviate this problem. For example, tu2016modeling and Mi:2016:EMNLP employ coverage vector as a lexical-level indicator to indicate whether a source word is translated or not. Zheng:2018:TACL and Meng:2018:IJCAI move one step further and directly model translated and untranslated source contents by operating on the attention context vector. He:2017:NIPS use a prediction network to estimate the future cost of translating the uncovered source words. Our approach is complementary to theirs since they model the adequacy learning at the word-level inside the generator (i.e., NMT models), while we model it at the sequence-level outside the generator. We take the representative coverage mechanism @cite_16 as another stronger baseline model for its simplicity and efficiency, and experimental results show that our model can further improve performance.
@cite_16
2410217169
Neural machine translation (NMT) often makes mistakes in translating low-frequency content words that are essential to understanding the meaning of the sentence. We propose a method to alleviate this problem by augmenting NMT systems with discrete translation lexicons that efficiently encode translations of these low-frequency words. We describe a method to calculate the lexicon probability of the next word in the translation candidate by using the attention vector of the NMT model to select which source word lexical probabilities the model should focus on. We test two methods to combine this probability with the standard NMT probability: (1) using it as a bias, and (2) linear interpolation. Experiments on two corpora show an improvement of 2.0-2.3 BLEU and 0.13-0.44 NIST score, and faster convergence time.