Simple, Scalable Adaptation for Neural Machine Translation
Table 1: Domain adaptation performance with different adaptation strategies. Base refers to the baseline NMT model trained on the WMT’14 En-Fr training corpus. FT refers to the fine-tuning upper bound, adapting all the model parameters by incrementally training on in-domain training data. LHUC adds additional task-specific gating parameters to the pre-trained model, which are trained on the in-domain data, as described in Vilar (2018). Adap. is the proposed adaptation approach, adding domain specific adapter layers trained on the in-domain training corpus.
['Dataset', 'Base', 'FT', 'LHUC', 'Adap.']
LHUC also improves performance over the base model, but lags behind a fully fine-tuned model for both domains and model capacities.