Deconvolution-Based Global Decoding for Neural Machine Translation
Table 1: Results of our model and the baselines (the results are those reported in the referred articles, and the models are trained on the identical training data or larger training data) on the Chinese-to-English translation, tested on the NIST Machine Translation tasks in 2003, 2004, 2005, 2006 by BLEU score evaluation.
['Model', 'MT-03', 'MT-04', 'MT-05', 'MT-06', 'Ave.']
Beside our reimplementation of the attention-based Seq2Seq model, we report the results of the recent NMT models, which are results in their original articles or improved results of the reimplementation. To facilitate fair comparison, we compare with the baselines that are trained on the same training data. The results have shown that for the NIST 2003, 2004, 2005 and 2006 translation tasks, our model with the deconvolution-based decoder outperforms the baselines, and the advantage of BLEU score over the attention-based Seq2Seq model is 2.82 on average compared with our reimplementation of the attention-based Seq2Seq model. From the results mentioned above, it can be inferred that the global information of the target-side contexts retrieved from the deconvolution-based decoder is contributive to the translation. Our analysis and case study in the following can further demonstrate how the deconvolution-based decoder improves the attention-based Seq2Seq model.