On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation
This work provides practical guidelines for improving domain adaptation efficiency in NLP, though it is incremental as it builds on existing training phases.
The paper investigates the interplay between data selection and fine-tuning in domain adaptation for language modeling and machine translation, finding that optimal data selection should be similar to the target domain but not too similar to preserve fine-tuning benefits, and that domain classifier-based selection outperforms contrastive methods.
Domain adaptation of neural networks commonly relies on three training phases: pretraining, selected data training and then fine tuning. Data selection improves target domain generalization by training further on pretraining data identified by relying on a small sample of target domain data. This work examines the benefit of data selection for language modeling and machine translation. Our experiments assess the complementarity of selection with fine tuning and result in practical recommendations: (i) selected data must be similar to the fine-tuning domain but not so much as to erode the complementary effect of fine-tuning; (ii) there is a trade-off between selecting little data for fast but limited progress or much data for slow but long lasting progress; (iii) data selection can be applied early during pretraining, with performance gains comparable to long pretraining session; (iv) data selection from domain classifiers is often more effective than the popular contrastive data selection method.