Span-based dependency-enhanced graph convolutional network for aspect sentiment triplet extraction

Aspect sentiment triplet extraction task detects three elements of fine-grained sentiment analysis from given sentence, including aspect and opinion terms and their sentiment polarity. Existing methods mainly include tagging-based and span-based methods, where the former show defects on handling overlapped triplets, and the latter could theoretically handle all overlapped triplets but lack of tailored inter-word dependency so that suffer from insufficient span semantic. In this paper, we propose a span-based dependency-enhanced graph convolutional network, which leverages contextual semantic and latent dependency to enrich span representations. Specifically, we devise a latent graph convolutional network to emphasize critical inter-word dependencies and cut off redundant connections in a learnable gating manner, improving the information flow during inter-word interaction. In addition, considering the problem of multi-word term sentiment consistency, we detect effective aspect and opinion terms derived from the output of span enumeration, and introduce term-level interactions by coupling, which meanwhile enables our model to deal with various types of triplets including many-to-one and one-to-many overlapped triplets. Extensive experiments over four benchmark datasets verify that the proposed method outperforms all the baselines with an average F1 improvement of up to 6.13%, and meanwhile shows fine interpretability. The experimental results demonstrate that effectively enhancing token-level and term-level interactions can significantly improve the aspect sentiment triplet extraction performance.