HIM: An End-to-End Hierarchical Interaction Model for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is an emerging task of fine-grained sentiment analysis, which aims to extract aspect terms, associated opinion terms, and sentiment polarities in the form of triplets. Thus, ASTE involves two groups of subtasks: aspect/opinion term extraction and aspect-opinion-pair sentiment classification. Due to the high correlations of subtasks, three categories of joint methods have been proposed, including end-to-end tagging-based methods, cascaded span-based methods, and sequence-to-sequence generation-based methods. These methods basically learn either a shared feature space or a shared sentence encoder to capture interactions across all subtasks by parameter sharing. However, they fail to learn deep and mutual interactive features for ASTE. In this work, we present a novel tagging scheme to cast ASTE as a unified boundary-words relation classification problem. Subsequently, we propose an end-to-end Hierarchical Interaction Model (HIM), exploiting deep and mutual interactions across subtasks mainly with two interaction modules. The first-level interaction module primarily leverages multi-task learning models to capture implicit subtask interactions. Then, the second-level interaction module, namely Gated Interaction Network (GIN), adopts a novel gated control mechanism and a newly-designed Conditional BiLSTM (Cond-BiLSTM) network to capture explicit subtask interactions. Moreover, to refine the unreliable outputs of the first-level module, we develop a General word-Pair Relationship Learning (G-PRL) component. With the task-shared features as input, G-PRL further facilitates interactions between term extraction and pair classification. We conduct experiments on two benchmarks and achieve promising results. Extensive analyses demonstrate the effectiveness and flexibility of our work.