Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax
Table 3: BLEU scores on WMT testing sets of the NMT models trained on original UN pairs (Oracle) and on two versions of mined UN corpora at sentence level. (*) indicates p<0.001 compared to the model trained on oracle pairs.
['[EMPTY]', '[BOLD] en-fr', '[BOLD] en-es']
We evaluate the sentence pairs mined from the forward and backward directions separately. All models perform very similarly, scoring within 2 BLEU points of each other. The one trained with pairs mined from the backward search achieves the highest performance in both language pairs. It is surprising to see that the model trained on mined data is even better than the model trained on original data. To investigate why, we examined some “false positive” examples returned by the nearest neighbor search. We found that many of them actually are quite good translations, with only a few words different from the actual translation pairs, some of them are even better aligned with the original translation. Our model is apparently filtering out some noise in the original data.