Multi-source Domain Adaptation for Sentiment Classification with Granger Causal Inference

Min Yang, Ying Shen, Xiaojun Chen, Chengming Li

Abstract: In this paper, we propose a multi-source domain adaptation method with a Granger-causal objective (MDA-GC) for cross-domain sentiment classification. Specifically, for each source domain, we build an expert model by using a novel sentiment-guided capsule network, which captures the domain invariant knowledge that bridges the knowledge gap between the source and target domains. Then, an attention mechanism is devised to assign importance weights to a mixture of experts, each of which specializes in a different source domain. In addition, we propose a Granger causal objective to make the weights assigned to individual experts correlate strongly with their contributions to the decision at hand. Experimental results on a benchmark dataset demonstrate that the proposed MDA-GC model significantly outperforms the compared methods.