Coverage Embedding Models for Neural Machine Translation
Table 1: Single system results in terms of (Ter-Bleu)/2 (the lower the better) on 5 million Chinese to English training set. NMT results are on a large vocabulary (300k) and with UNK replaced. UGRU: updating with a GRU; USub: updating as a subtraction; UGRU + USub: combination of two methods (do not share coverage embedding vectors); +Obj.: UGRU + USub with an additional objective in Equation 6, we have two λs for UGRU and USub separately, and we test λGRU = 1×10−4 and λSub = 1×10−2.
['single system', 'single system', 'MT06', 'MT06', 'MT06', 'MT08 News', 'MT08 News', 'MT08 News', 'MT08 Web', 'MT08 Web', 'MT08 Web', 'avg.']
The traditional syntax-based system achieves 9.45, 12.90, and 17.72 on MT06, MT08 News, and MT08 Web sets respectively, and 13.36 on average in terms of (Ter- Bleu)/2. The large-vocabulary NMT (LVNMT), our baseline, achieves an average (Ter- Bleu)/2 score of 15.74, which is about 2 points worse than the hybrid system.