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The practical success of much of NLP depends on the availability of trainingdata. However, in real-world scenarios, training data is often scarce, notleast because many application domains are restricted and specific. In thiswork, we compare different methods to handle this problem and provideguidelines for building NLP applications when there is only a small amount oflabeled training data available for a specific domain. While transfer learningwith pre-trained language models outperforms other methods across tasks,alternatives do not perform much worse while requiring much less computationaleffort, thus significantly reducing monetary and environmental cost. We examinethe performance tradeoffs of several such alternatives, including models thatcan be trained up to 175K times faster and do not require a single GPU.

Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?