Instance Weighting for Domain Adaptation in NLP

TLDR

Domain adaptation in NLP is critical because novel domains lack labeled data, and it can be formally characterized as requiring adaptation to differing instance and classification function distributions between source and target. The study investigates domain adaptation from an instance‑weighting perspective. The authors propose a general instance‑weighting framework that addresses the distinct distributional needs of instances and classification functions in source and target domains. Empirical results on three NLP tasks demonstrate that instance weighting effectively leverages target‑domain information to improve adaptation.

Abstract

Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting perspective. We formally analyze and characterize the domain adaptation problem from a distributional view, and show that there are two distinct needs for adaptation, corresponding to the different distributions of instances and classification functions in the source and the target domains. We then propose a general instance weighting framework for domain adaptation. Our empirical results on three NLP tasks show that incorporating and exploiting more information from the target domain through instance weighting is effective.