Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
Table 3: Results on the Chinese-English word alignment task. The evaluation metric is alignment error rate. “**”: significantly better than RNNsearch with independent training (p<0.01).
['Training', 'C → E', 'E → C']
We follow Luong et al. \shortciteLuong:15 to “force-decode” our jointly trained models to produce translations that match the references. Then, we extract only one-to-one alignments by selecting the source word with the highest alignment weight for each target word. There is still room for improvement in attention accuracy.