Bi-Decoder Augmented Network for Neural Machine Translation
Table 3: BLEU scores on WMT14 English-to-French for NMT models with different encoders.
['[BOLD] Encoder Source', '[BOLD] BLEU ( [ITALIC] p1)', '[BOLD] BLEU ( [ITALIC] p2)']
We train the NMT model on WMT14 English-to-French dataset, and then replace the well-trained encoder with different sources at the testing time. We first replace the encoder with random parameters and the results drop a lot, which is not surprising. Then we use the encoder trained on the WMT14 English-to-German dataset to test the performance of cross language performance. The results improve modestly compared to the random encoder but still have a huge gap to the original model. This indicates that the coarse cross language training doesn’t work even though the languages are quite similar. Finally, we replace the original encoder with the encoder from our BiDAN framework, which is also trained on the WMT14 English-to-German dataset. Although the performance is still far away from the level which is achieved by the original encoder, it is much better than the previous two methods. We point out that the only difference among those models are the parameters of the encoder, which determine the text representation. Thus the different performances demonstrate that our model provides a more language-dependent text representation, and this may also explain the improvement of our structure to the general NMT model.