Domain Adaptive Inference for Neural Machine Translation
Table 7: Total BLEU for test data concatenated across domains. Results from 2-model es-en and 3-model en-de ensembles, compared to oracle model chosen if test domain is known. No-reg uniform corresponds to the approach of Freitag and Al-Onaizan (2016). BI+IS performs similarly to strong oracles with no test domain labeling.
['[BOLD] Language pair', '[BOLD] Model type', '[BOLD] Oracle model', '[BOLD] Decoder configuration  [BOLD] Uniform', '[BOLD] Decoder configuration  [BOLD] BI + IS']
Uniform no-reg ensembling outperforms unadapted uniform ensembling, since fine-tuning gives better in-domain performance. [CONTINUE] BI+IS decoding with single-domain trained models achieves gains over both the naive uniform approach and over oracle single-domain models. BI+IS with EWC-adapted models gives a 0.9 / 3.4 BLEU gain over the strong uniform EWC ensemble, and a 2.4 / 10.2 overall BLEU gain over the approach described in Freitag and Al-Onaizan (2016).