Semi-supervised Sentiment Classification Based on Auxiliary Task Learning

Abstract

Sentiment classification is an important task in the community of Nature Language Processing. This task aims to determine the sentiment category towards a piece of text. One challenging problem of this task is that it is difficult to obtain a large number of labeled samples. Therefore, a large number of studies are focused on semi-supervised learning, i.e., learning information from unlabeled samples. However, one disadvantage of the previous methods is that the unlabeled samples and the labeled samples are studied in different models, and there is no interaction between them. Based on this, this paper tackles the problem by proposing a semi-supervised sentiment classification based on auxiliary task learning, namely Aux-LSTM, which is used to assist learning the sentiment classification task with a small amount of human-annotated samples by training auto-annotated samples. Specifically, the two tasks are allowed to share the auxiliary LSTM layer, and the auxiliary expression obtained by the auxiliary LSTM layer is used to assist the main task. Empirical studies demonstrate that the proposed method can effectively improve the experimental performance.