  Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a
sentence, including target entities, associated sentiment polarities, and
opinion spans which rationalize the polarities. Existing methods are short on
building correlation between target-opinion pairs, and neglect the mutual
interference among different sentiment triplets. To address these issues, we
utilize a two-stage framework to enhance the correlation between targets and
opinions: at stage one, we extract targets and opinions through sequence
tagging; then we append a group of artificial tags named Perceivable Pair,
which indicate the span of a specific target-opinion tuple, to the input
sentence to obtain closer correlated target-opinion pair representation.
Meanwhile, we reduce the negative interference between triplets by restricting
tokens' attention field. Finally, the polarity is identified according to the
representation of the Perceivable Pair. We conduct experiments on four
datasets, and the experimental results show the effectiveness of our model.
