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.
|Title of host publication||The 6th Workshop on Representation Learning for NLP : Proceedings of the Workshop (REPL4NLP 2021)|
|Number of pages||7|
|Place of Publication||Stroudsburg, PA|
|Publisher||Association for Computational Linguistics|
|Publication status||Published - 2021|