IDCN: A Novel Interactive Dual Channel Network for Aspect Sentiment Triplet Extraction

Abstract

Aspect sentiment triplet extraction (ASTE) is one of the important subtasks of aspect-based sentiment analysis, it aims at detecting the aspect terms, opinion terms, and the corresponding sentiment polarity, simultaneously. Most methods directly employ GCNs to capture the syntactic dependency information in ASTE. However, these methods may lead to error propagation. Besides, the GCN-based methods are weak at capturing sequence information and long-distance information. The general neural networks such as LSTM are good at capturing this kind of information. However, these general neural networks are weak at modeling syntactic dependency information. To alleviate the above problems, we propose a novel interactive dual channel network (IDCN) for ASTE. In IDCN, an interactive word pair generating (IWPG) module is designed to model the sequence information, long-distance dependency information, and correlation relations between word pairs, simultaneously. In the IWPG module, the dual channels can learn different representations. Based on these representations, the informative word-pair representations can be learned by the interaction mechanism of dual channels. Besides, we design the syntactic dependency fusion module to model the syntax dependency information by constructing word pair dependency relation tensors and pooling mechanism, which can naturally inject the syntactic dependency knowledge into the general neural networks and reduce error propagation. Abundant experiments have been performed on multiple datasets. The experimental results show that IDCN acquires state-of-the-art results and validates the effectiveness of IDCN.