Knowledge complementation based dual-table filling approach for aspect sentiment triplet extraction

Abstract

Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect terms, opinion terms, and the corresponding sentiment polarity of aspect-opinion pairs from review sentences. Current outperforming table-filling approaches focus on enhancing the relation representation within a single table, with various algorithms designed to exploit deeper information and additional knowledge. However, due to the sequential execution of these algorithms on a single table, the granularity of the knowledge evolves unidirectionally, inevitably leading to the loss of knowledge at different granularities from the previous stage of the entire process. To overcome this limitation, we propose a simple yet effective end-to-end model for the ASTE task, namely Dual-Table Filling (DTF), which constructs two complementary tables instead of just one to capture different granularities of knowledge in a sentence. Specifically, the main table exploits latent knowledge and hidden dependencies, while the assistant table captures more fine-grained contextual knowledge. This structure allows the model to utilize knowledge at multiple granularities more efficiently while maintaining simplicity. In addition, a Syntactic Enhanced Encoder (SEE) is developed to acquire syntactic-enhanced knowledge from sentences, enhancing the comprehension of sentence structure and word relationships. Experimental results on public benchmark datasets show the effectiveness and exceptional performance of our proposed model.