Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
Table 2: Results on the Chinese-English translation task. Moses is a phrase-based statistical machine translation system. RNNsearch is an attention-based neural machine translation system. We introduce agreement-based joint training for bidirectional attention-based NMT. NIST06 is the validation set and NIST02-05, 08 are test sets. The BLEU scores are case-insensitive. “*”: significantly better than Moses (p<0.05); “**”: significantly better than Moses (p<0.01); “+”: significantly better than RNNsearch with independent training (p<0.05); “++”: significantly better than RNNsearch with independent training (p<0.01). We use the statistical significance test with paired bootstrap resampling [Koehn2004].
['System', 'Training', 'Direction', 'NIST06', 'NIST02', 'NIST03', 'NIST04', 'NIST05', 'NIST08']
We find that RNNsearch generally outperforms Moses except for the C → E direction on the NIST08 test set, which confirms the effectiveness of attention-based NMT on distantly-related language pairs such as Chinese and English.