Domain Adaptive Inference for Neural Machine Translation
Table 5: Test BLEU for 2-model es-en and 3-model en-de unadapted model ensembling, compared to oracle unadapted model chosen if test domain is known. Uniform ensembling generally underperforms the oracle, while BI+IS outperforms the oracle.
['[BOLD] Decoder configuration', '[BOLD] es-en  [BOLD] Health', '[BOLD] es-en  [BOLD] Bio', '[BOLD] en-de  [BOLD] News', '[BOLD] en-de  [BOLD] TED', '[BOLD] en-de  [BOLD] IT']
Table 5 shows improvements on data without domain labelling using our adaptive decoding schemes with unadapted models trained only on one domain [CONTINUE] Uniform ensembling under-performs all oracle models except es-en Bio, especially on general domains. Identity-BI strongly improves over uniform ensembling, and BI with λ as in Eq. 10 improves further for all but es-en Bio. BI and IS both individually outperform the oracle for all but IS-News, [CONTINUE] With adaptive decoding, we do not need to assume whether a uniform ensemble or a single model might perform better for some potentially unknown domain. We highlight this in Table 7 by reporting results with the ensembles of Tables 5 and 6 over concatenated test sets, to mimic the realistic scenario of unlabelled test data. We additionally include the uniform no-reg ensembling