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