Joint aspect terms extraction and aspect categories detection via multi-task learning

Abstract

Aspect Terms Extraction (ATE) and Aspect Categories Detection (ACD) are two fundamental sub-tasks for aspect-based sentiment analysis. Most of the existing works mainly focus on the ATE task or the co-extraction of aspect terms and opinion words, while few attention are paid to the ACD task. In this work, we propose a joint model to seamlessly integrate the ATE and ACD tasks into a multi-task learning framework. Each of the tasks is based on multi-layer Convolutional Neural Networks (CNNs) for computing high-level word representations, and produces a task-specific and a task-share vector. The task-share vector of one task is used to propagate information to the other, and guides the counterpart task to align the informative textual features to produce the task-specific vectors. Finally, a fully-connected layer with a softmax/sigmoid function is applied to the task-specific vectors for the specific information extraction. The rationale underlying the proposed joint model is that, aspect terms and aspect categories are semantically related, and the information propagated between the two tasks can help to capture the semantic alignments between the aspect terms and categories, and produce informative task-specific vectors. Moreover, the ATE task models local semantics at each position of a sentence, while the ACD task extracts global features of the whole sentence. The mutual interactions between local and global features, therefore, can reciprocally capture informative textual features for the information extraction tasks. We validate the effectiveness of the proposed model on two widely used datasets, and show its advantage over the state-of-the-art baselines. We also investigate the effectiveness of the multi-task framework by comparing the proposed model with its variants. Further, we study the robustness of the proposed model by presenting the model performance with respect to different hyperparameters. Finally, we provide visualization examples to gain a better understanding of the advantages the multi-task learning scheme.