Domain Differential Adaptation for Neural Machine Translation
Table 5: BLEU points of models after continued training on the IT development dataset with different values of coverage penalty β.
['Coverage penalty  [ITALIC] β', '0.00', '0.05', '0.10', '0.15', '0.20', '0.25', '0.30']
This sheds light on some limitations of our proposed method, which we will discuss in the next section. To verify this hypothesis, under the continued training setting we adopt a decoding-time coverage penalty Wu et al. The best DDA-Deep method outperforms the baseline by 1.03 BLEU points.