Abstract: 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.