Look-ahead Attention for Generation in Neural Machine Translation
Table 1: Translation results (BLEU score) for Chinese-to-English translation. “†”: significantly better than NMT Baseline (p<0.05). “‡”: significantly better than NMT Baseline (p<0.01).
['System', 'MT04', 'MT05', 'MT06', 'Ave']
We list the BLEU scores of our proposed model in Moses-1 Moses-2 is the same as Moses-1 except that the language model is trained using the target data plus 10 M Xinhua portion of Gigaword corpus. Moreover, our Enc-Dec pattern gets an improvement of +0.93 BLEU points over the state-of-the-art NMT baseline, which demonstrates that the look-ahead attention mechanism is effective for generation in conventional attention-based NMT.