Graph Convolutional Networks for Target-oriented Opinion Words Extraction with Adversarial Training

The task of Target-oriented Opinion Words Extraction aims to extract the corresponding opinion words for a given opinion target from the sentence. Recently, the methods based on recurrent neural networks have shown promising results for this task. However, these approaches only considered the sequential information of the sentences and ignored the syntactic structure. In this paper, we propose a novel graph convolutional network with adversarial training to extract the opinion words. We present a graph convolutional network based on dependency tree to learn the syntactic representation of the input. Besides, we train our model with the mixture of original examples and adversarial examples, which can improve the robustness of the model. We conduct experiments on four benchmarking datasets and the results illustrate that our proposed model consistently outperforms the state-of-the-art methods.