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Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. It aims at classifying the sentiment expressed on some target aspects/features of entities (e.g., products and services). Although a great deal of research has been done, this task remains to be very challenging. Recently, memory networks, a type of neural model, have been used for this task and have achieved state-of-the-art results. However, such neural models usually require a large amount of well-annotated training data for producing reasonably good results. Unfortunately, for the ASC task, the human-annotated data with aspect-level labels are scarce and costly to obtain. In this work, we aim to use big unlabeled data to help. The key idea is to make a memory network learn knowledge from the big unlabeled data (treated as past tasks) and use the learned knowledge to better guide its future task learning. To achieve this goal, we …