Neural Machine Translation Training in a Multi-Domain Scenario
Table 3: Comparing results of systems built on a concatenation of the data. OD represents a concatenation of the out-of-domain corpora and ALL represents a concatenation of OD and the in-domain data. → sign means fine-tuning
['[BOLD] Arabic-English', '[BOLD] Arabic-English TED', '[BOLD] Arabic-English ALL', '[BOLD] Arabic-English OD→TED', '[BOLD] Arabic-English ALL→TED']
Next we evaluated how the models performed when trained on concatenated data. We mainly tried two variations: i) concatenating all the available data (ALL) ii) combine only the available out-of-domain data (OD) and later fine-tune the model on the in-domain data. The fine-tuned system outperformed a full concatenated system by 1.8 and 2.1 average BLEU points in Arabic-English and German-English systems respectively.