Related papers: Predicting the Success of Domain Adaptation in Text Similarity

Predicting the Success of Domain Adaptation in Text Similarity

URL: http://arxiv.org/abs/2106.04641v1
Date: Tue, 8 Jun 2021 19:02:15 GMT
Title: Predicting the Success of Domain Adaptation in Text Similarity
Authors: Nicolai Pogrebnyakov, Shohreh Shaghaghian
Abstract summary: This paper models adaptation success and selection of the most suitable source domains among several candidates in text similarity. While mostly positive, the results also point to some domains where adaptation success was difficult to predict.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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