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Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysistask which aims to extract the aspects from sentences and identify theircorresponding sentiments. Aspect term extraction (ATE) is the crucial step forABSA. Due to the expensive annotation for aspect terms, we often lack labeledtarget domain data for fine-tuning. To address this problem, many approacheshave been proposed recently to transfer common knowledge in an unsupervisedway, but such methods have too many modules and require expensive multi-stagepreprocessing. In this paper, we propose a simple but effective technique basedon mutual information maximization, which can serve as an additional componentto enhance any kind of model for cross-domain ABSA and ATE. Furthermore, weprovide some analysis of this approach. Experiment results show that ourproposed method outperforms the state-of-the-art methods for cross-domain ABSAby 4.32% Micro-F1 on average over 10 different domain pairs. Apart from that,our method can be extended to other sequence labeling tasks, such as namedentity recognition (NER).

A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis