Explicit Interaction Network for Aspect Sentiment Triplet Extraction
This work addresses the problem of extracting sentiment triplets from text for natural language processing applications, representing an incremental improvement with a novel method for known bottlenecks.
The paper tackles Aspect Sentiment Triplet Extraction (ASTE) by dividing it into target-opinion joint detection and sentiment classification subtasks, using sequence and table encoders to capture compositional features and explicit interactions, resulting in outperforming state-of-the-art methods on six datasets.
Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their sentiment polarities and opinions explaining the sentiment from a sentence. ASTE could be naturally divided into 3 atom subtasks, namely target detection, opinion detection and sentiment classification. We argue that the proper subtask combination, compositional feature extraction for target-opinion pairs, and interaction between subtasks would be the key to success. Prior work, however, may fail on `one-to-many' or `many-to-one' situations or derive non-existent sentiment triplets due to defective subtask formulation, sub-optimal feature representation or the lack of subtask interaction. In this paper, we divide ASTE into target-opinion joint detection and sentiment classification subtasks, which is in line with human cognition, and correspondingly utilize sequence encoder and table encoder to handle them. Table encoder extracts sentiment at token-pair level, so that the compositional feature between targets and opinions can be easily captured. To establish explicit interaction between subtasks, we utilize the table representation to guide the sequence encoding, and inject the sequence features back into the table encoder. Experiments show that our model outperforms state-of-the-art methods on six popular ASTE datasets.