Bag-of-Words as Target for Neural Machine Translation
Table 2: Results of our model and the baselines (directly reported in the referred articles) on the Chinese-English translation. “-” means that the studies did not test the models on the corresponding datasets.
['Model', 'MT-02', 'MT-03', 'MT-04', 'MT-05', 'MT-06', 'MT-08', 'All']
We compare our model with our implementation of Seq2Seq+Attention model. For fair comparison, the experimental setting of Seq2Seq+Attention is the same as BAT, so that we can regard it as our proposed model removing the bag-of-words target. The results show that our model achieves the BLEU score of 36.51 on the total test sets, which outperforms the Seq2Seq baseline by the BLEU of 4.55. In order to further evaluate the performance of our model, we compare our model with the recent NMT systems which have been evaluated on the same training set and the test sets as ours. Their results are directly reported in the referred articles.