  Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a
given sentence's triplets, which consist of aspects, opinions, and sentiments.
Recent studies tend to address this task with a table-filling paradigm, wherein
word relations are encoded in a two-dimensional table, and the process involves
clarifying all the individual cells to extract triples. However, these studies
ignore the deep interaction between neighbor cells, which we find quite helpful
for accurate extraction. To this end, we propose a novel model for the ASTE
task, called Prompt-based Tri-Channel Graph Convolution Neural Network
(PT-GCN), which converts the relation table into a graph to explore more
comprehensive relational information. Specifically, we treat the original table
cells as nodes and utilize a prompt attention score computation module to
determine the edges' weights. This enables us to construct a target-aware
grid-like graph to enhance the overall extraction process. After that, a
triple-channel convolution module is conducted to extract precise sentiment
knowledge. Extensive experiments on the benchmark datasets show that our model
achieves state-of-the-art performance. The code is available at
https://github.com/KunPunCN/PT-GCN.
