  Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA
which outputs triplets of an aspect target, its associated sentiment, and the
corresponding opinion term. Recent models perform the triplet extraction in an
end-to-end manner but heavily rely on the interactions between each target word
and opinion word. Thereby, they cannot perform well on targets and opinions
which contain multiple words. Our proposed span-level approach explicitly
considers the interaction between the whole spans of targets and opinions when
predicting their sentiment relation. Thus, it can make predictions with the
semantics of whole spans, ensuring better sentiment consistency. To ease the
high computational cost caused by span enumeration, we propose a dual-channel
span pruning strategy by incorporating supervision from the Aspect Term
Extraction (ATE) and Opinion Term Extraction (OTE) tasks. This strategy not
only improves computational efficiency but also distinguishes the opinion and
target spans more properly. Our framework simultaneously achieves strong
performance for the ASTE as well as ATE and OTE tasks. In particular, our
analysis shows that our span-level approach achieves more significant
improvements over the baselines on triplets with multi-word targets or
opinions.
