Word-based Domain Adaptation for Neural Machine Translation
Table 3: E-commerce English → Chinese BLEU results on test set. Baseline is trained on mixed in-domain and out-of-domain data. No. 2 is continuing training from baseline with objective defined as Eq. 1. No. 3 is continuing training from baseline with sentence-level weights and No. 4 is with word weights, as defined in Section 2.2. No. 5 refers to assigning wt using LCW method described in Section 2.3. No. 6 is equivalent to directly fine-tuning on in-domain datasets starting from the baseline model and No. 7 is equivalent to fine-tuning on in-domain datasets after No. 5 is finished.
['No.', 'System description', 'Item descriptions BLEU [%]', 'Item descriptions TER [%]']
First, the baseline trained on mixed in-domain and out-of-domain datasets gives 24.37% BLEU and 61.66% TER, respectively. Directly fine-tuning on in-domain dataset already improves the model due to the bias of the model towards in-domain data.