  Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis
task which aims to extract the aspects from sentences and identify their
corresponding sentiments. Aspect term extraction (ATE) is the crucial step for
ABSA. Due to the expensive annotation for aspect terms, we often lack labeled
target domain data for fine-tuning. To address this problem, many approaches
have been proposed recently to transfer common knowledge in an unsupervised
way, but such methods have too many modules and require expensive multi-stage
preprocessing. In this paper, we propose a simple but effective technique based
on mutual information maximization, which can serve as an additional component
to enhance any kind of model for cross-domain ABSA and ATE. Furthermore, we
provide some analysis of this approach. Experiment results show that our
proposed method outperforms the state-of-the-art methods for cross-domain ABSA
by 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 named
entity recognition (NER).
