Transfer learning methods, and in particular domain adaptation, help exploit labeled data in one domain to improve the performance of a certain task in another domain. However, it is still not clear what factors affect the success of domain adaptation. This paper models adaptation success and selection of the most suitable source domains among several candidates in text similarity. We use descriptive domain information and cross-domain similarity metrics as predictive features. While mostly positive, the results also point to some domains where adaptation success was difficult to predict.

翻译：转让学习方法,特别是领域适应,有助于利用一个领域的标记数据改进另一个领域的某一任务的执行情况,然而,尚不清楚哪些因素影响领域适应的成功。本文模拟了适应的成功,并在文本相似的几种候选人中选择了最合适的来源领域。我们使用描述性领域信息和交叉性类似指标作为预测性特征。结果虽然大多是积极的,但也指向适应成功难以预测的一些领域。