WIND: Weighting Instances Differentially for Model-Agnostic Domain Adaptation

Abstract

Domain Adaptation is a fundamental problem in machine learning and natural language processing. In this paper, we study the domain adaptation problem from the perspective of instance weighting. Conventional instance weighting approaches cannot learn the weights which make the model generalize well in target domain. To tackle this problem, inspired by meta-learning, we formulate the domain adaptation problem as a bi-level optimization problem, and propose a novel differentiable model-agnostic instance weighting algorithm. Our proposed approach can automatically learn the instance weights instead of using manually designed weighting metrics. To reduce the computational complexity, we adopt the second-order approximation technique during training. Experimental results on three different NLP tasks (Sentiment Classification, Neural Machine Translation and Relation Extraction) illustrate the efficacy of our proposed method.