Bridging the Gap between Training and Inference for Neural Machine Translation
Table 3: Case-sensitive BLEU scores (%) on En→De task. The “‡” indicates the results are significantly better (p<0.01) than RNNsearch and Transformer.
['[BOLD] Systems', 'newstest2014']
We also evaluate our approach on the WMT’14 benchmarks on the En→De translation task. Similar with results on the Zh→En task, both scheduled sampling and MIXER could improve the two baseline systems. Our method improves the RNNSearch and Transformer baseline models by +1.59 and +1.31 BLEU points respectively. These results demonstrate that our model works well across different language pairs.