Natural language processing (NLP) algorithms are rapidly improving but often struggle when applied to out-of-distribution examples. A prominent approach to mitigate the domain gap is domain adaptation, where a model trained on a source domain is adapted to a new target domain. We present a new learning setup, ``domain adaptation from scratch'', which we believe to be crucial for extending the reach of NLP to sensitive domains in a privacy-preserving manner. In this setup, we aim to efficiently annotate data from a set of source domains such that the trained model performs well on a sensitive target domain from which data is unavailable for annotation. Our study compares several approaches for this challenging setup, ranging from data selection and domain adaptation algorithms to active learning paradigms, on two NLP tasks: sentiment analysis and Named Entity Recognition. Our results suggest that using the abovementioned approaches eases the domain gap, and combining them further improves the results.

提出了一种新的学习方式：从头开始进行领域自适应，以便在隐私保护的情况下将NLP扩展到敏感领域。研究比较了几种方法，包括数据选择和领域自适应算法以及主动学习方法，在情感分析和命名实体识别等两个NLP任务上进行了验证，结果表明结合上述方法可以缓解领域差距并进一步提高结果。

从头开始的域自适应