Deconvolution-Based Global Decoding for Neural Machine Translation
Table 2: Results of our model and the baselines (directly reported in the referred articles) on the English-to-Vietnamese translation, tested on the TED tst2013 with the BLEU score evaluation.
['Model', 'BLEU']
Compared with the attention-based Seq2Seq model, including the implementation with the strongest performance, our model with the deconvolution-based decoder can outperform it with the advantage of BLEU score 1.54. We also display the most recent model NPMT (nplm) trained and tested on the dataset. Compared with NPMT, our model has an advantage of BLEU score 0.78. It can be indicated that for the low-resource translation, the information from the deconvolution-based decoder is important, which brings significant improvement to the conventional attention-based Seq2Seq model.