Coverage Embedding Models for Neural Machine Translation
Table 2: Single system results in terms of (Ter-Bleu)/2 on 11 million set. NMT results are on a large vocabulary (500k) and with UNK replaced. Due to the time limitation, we only have the results of UGRU system.
['single system', 'single system', 'MT06', 'MT06', 'MT08 News', 'MT08 News', 'MT08 Web', 'MT08 Web', 'avg.']
The result of our UGRU coverage model gives almost 1 point gain over LVNMT. Those results suggest that the more training data we use, the stronger the baseline system becomes, and the harder to get improvements. In order to get a reasonable or strong NMT system, we have to conduct experiments over a large-scale training set.