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Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opiniontriplets, consisting of an opinion target or aspect, its associated sentiment,and the corresponding opinion term/span explaining the rationale behind thesentiment. Existing research efforts are majorly tagging-based. Among themethods taking a sequence tagging approach, some fail to capture the stronginterdependence between the three opinion factors, whereas others fall short ofidentifying triplets with overlapping aspect/opinion spans. A recent gridtagging approach on the other hand fails to capture the span-level semanticswhile predicting the sentiment between an aspect-opinion pair. Different fromthese, we present a tagging-free solution for the task, while addressing thelimitations of the existing works. We adapt an encoder-decoder architecturewith a Pointer Network-based decoding framework that generates an entireopinion triplet at each time step thereby making our solution end-to-end.Interactions between the aspects and opinions are effectively captured by thedecoder by considering their entire detected spans while predicting theirconnecting sentiment. Extensive experiments on several benchmark datasetsestablish the better efficacy of our proposed approach, especially in therecall, and in predicting multiple and aspect/opinion-overlapped triplets fromthe same review sentence. We report our results both with and without BERT andalso demonstrate the utility of domain-specific BERT post-training for thetask.

PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction