Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation
Table 1: Case-insensitive BLEU scores (%) on NIST Zh\RightarrowEn task. Domain differences between MT02 and MT06 cause our model to decrease slightly over ABDNMT{}^{*} on MT06.
['System', 'Architecture', '[BOLD] # Para.', 'MT02', 'MT03', 'MT04', 'MT05', 'MT06', 'Ave.', '\\Delta']
Our model achieves +3.23 BLEU improvements over RNNSearch{}^{*}, which proves exploiting R2L target semantics can effectively enhance the L2R translation quality. ABDNMT{}^{*} is a strong bidirectional decoding baseline that surpasses ABDNMT by +4.75 BLEU on average due to advanced techniques. Our model can still surpass ABDNMT{}^{*} by +1.04 BLEU on average, which proves the effectiveness of our method. Besides, our model surpasses the Transformer model by +1.10 BLEU points with only 63% parameters compared to it (56.36M vs. 90.64M).