Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
Table 1: BLEU and TER scores on the NIST Chinese-English translation tasks. The BLEU scores are case-insensitive. Avg means the average scores on all test sets. “‡”: statistically better than RNNSearch* (p < 0.01). Higher BLEU (or lower TER) scores indicate better translation quality.
['[EMPTY]', '[BOLD] Model', '[BOLD] NIST06', '[BOLD] NIST02', '[BOLD] NIST03', '[BOLD] NIST04', '[BOLD] NIST08', '[BOLD] Avg']
Clearly, every one of the three NMT models we proposed, with some bridging method, improve the translation accuracy over all test sets in comparison to the SMT (cdec) and NMT (RNNSearch*) baseline systems.