Improving Aspect Sentiment Triplet Extraction with Perturbed Masking and Edge-Enhanced Sentiment Graph Attention Network

Songhua Yang, Tengxun Zhang, Hongfei Xu, Yuxiang Jia

Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from texts. Previous studies attempted to use an unsupervised perturbed masking technique to derive syntax trees from pre-trained language models (PLMs) to extract aspect sentiment simply. However, existing methods neglect further exploration of the technique for more complex tasks like ASTE. In this paper, we propose a novel Edge-enhanced Sentiment Graph Attention Network model (ES-GAT), which transforms the impact matrix generated by the technique into a new linguistic feature, and uses a novel effective fusion strategy to incorporate multiple features, which can better capture the implicit connections among sentiment elements. In the graph attention module, we simultaneously consider edge and node attention weight calculations and updates to solve the edge-sensitive end to end ASTE task. Experiments show that our model outperforms the strong baselines. The F1 scores are improved by 2.52% and 1.56% on average on the two versions of the benchmark datasets. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code and datasets are available at https://github.com/SupritYoung/ESGAT