Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
Table 3: The translation performance of the BR-CSGAN with different N for Monte Carlo search. ”-” means that the proposed model shows no improvement than the pre-trained generator or it can not be trained stably. With N set as 0, it is referred to as the pre-trained generator. Similarly, we only report results on the RNNSearch and λ is set as 0.7.
['[ITALIC] N', 'NIST02', 'NIST03', 'NIST04', 'NIST05']
We are also curious about how the sample times N for Monte Carlo search affects the translation performance. Intuitively, if N is set as a small number, the intermediate reward for each word may be incorrect since there is a large variance for the Monto Carol search when the sample time is too small. And if otherwise, the computation shall be very time consuming because we need to do much more sampling. Therefore, there is a trade-off between the accuracy and computation complexity here. We investigate this problem on the Chinese-English translation task. As a matter of fact, the translation performance of the model gets worse and worse. We conjecture that the approximated reward is far from the expected reward due to the large variance when N is set too small, and gives wrong gradient directions for model updating. Since the training for GAN is not stable, the wrong gradient direction exacerbates the unstableness and results in the BLEU getting worse and worse. With the increasing of N, the translation performance of the model gets improved. However, with N set larger than 20, we get little improvement than the model with N set as 20 and the training time exceeds our expectation.