Joint Training for Neural Machine Translation Models with Monolingual Data
Table 1: Case-insensitive BLEU scores (%) on Chinese↔English translation. The “Average” denotes the average BLEU score of all datasets in the same setting. The “C” and “E” denote Chinese and English respectively.
['Direction', 'System', 'NIST2006', 'NIST2003', 'NIST2005', 'NIST2008', 'NIST2012', 'Average']
All the results are reported based on case-insensitive BLEU. Compared with RNNSearch+M, our joint training approach introduces data weight to better handle poor pseudo-training data, and the joint interactive training can boost the models of two directions with the help of each other, instead of only use the target-to-source model to help source-to-target model. Our approach also yields better translation than SS-NMT with at least 0.93 points BLEU improvements on average. This result shows that our method can better make use of both source and target monolingual corpora than \citeauthorCheng2016SemiSupervisedLF \shortciteCheng2016SemiSupervisedLF’s approach.