Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
Table 1: BLEU score on Chinese-English and English-German translation tasks. The hyper-parameter λ is selected according to the development set. For the Transformer, following Vaswani et al. (2017), we report the result of a single model obtained by averaging the 5 checkpoints around the best model selected on the development set.
['Model', 'Chinese-English NIST03', 'Chinese-English NIST04', 'Chinese-English NIST05', 'Chinese-English average', 'English-German newstest2014']
+1.11 BLEU points averagely on Chinese-English test sets and +0.9 BLEU points on English-German test set. Armed with the BLEU objective, the BR-CSGAN (the line of RNNSearch+BR-CSGAN (λ=0.7)) leads to more significant improvements, +1.83 BLEU points averagely on Chinese-English translation and +1.69 BLEU points on English-German translation. We also test the translation performance when the RNNSearch is only guided by the static BLEU objective (the line of RNNSearch+BR-CSGAN (λ=0)), and we only get +0.58 BLEU points improvement on Chinese-English translation and +0.55 BLEU points improvement on English-German. Experiments on the Transformer show the same trends. While the Transformer has achieved state-of-the-art translation performances, our approach still achieves +0.81 BLEU points improvement on Chinese-English translation and +0.62 BLEU points improvement on English-German.