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.

方面术语提取（ATE）和方面类别检测（ACD）是用于基于方面的情感分析的两个基本子任务。现有的大多数工作主要集中在ATE任务或方面术语和见解词的共提取上，而对ACD任务的关注却很少。在这项工作中，我们提出了一个联合模型，以将ATE和ACD任务无缝集成到一个多任务学习框架中。每个任务都基于用于计算高级单词表示的多层卷积神经网络（CNN），并产生特定于任务的任务和任务共享向量。一个任务的任务共享向量用于将信息传播到另一任务，并指导对方任务对齐信息丰富的文本特征以生成特定于任务的向量。最后，将具有softmax / Sigmoid函数的全连接层应用于特定于任务的向量，以提取特定的信息。所提出的联合模型所基于的基本原理是，方面方面和方面类别在语义上相关，并且在两个任务之间传播的信息可以帮助捕获方面方面和类别之间的语义对齐，并产生信息性的任务特定向量。此外，ATE任务在句子的每个位置建模局部语义，而ACD任务则提取整个句子的全局特征。因此，局部和全局特征之间的相互交互可以相互捕获信息提取任务的信息性文本特征。我们在两个广泛使用的数据集上验证了该模型的有效性，并显示其相对于最新基准的优势。我们还通过比较建议的模型及其变体来研究多任务框架的有效性。此外，我们通过针对不同的超参数呈现模型性能来研究所提出模型的鲁棒性。最后，我们提供可视化示例，以更好地理解多任务学习方案的优势。