  Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in
sentiment analysis research, aiming to extract triplets of the aspect term, its
corresponding opinion term, and its associated sentiment polarity from a given
sentence. Recently, many neural networks based models with different tagging
schemes have been proposed, but almost all of them have their limitations:
heavily relying on 1) prior assumption that each word is only associated with a
single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level
interactions and treating each opinion/aspect as a set of independent words.
Hence, they perform poorly on the complex ASTE task, such as a word associated
with multiple roles or an aspect/opinion term with multiple words. Hence, we
propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract
sentiment triplets in span-level, where each span may consist of multiple words
and play different roles simultaneously. To this end, this paper formulates the
ASTE task as a multi-class span classification problem. Specifically, STAGE
generates more accurate aspect sentiment triplet extractions via exploring
span-level information and constraints, which consists of two components,
namely, span tagging scheme and greedy inference strategy. The former tag all
possible candidate spans based on a newly-defined tagging set. The latter
retrieves the aspect/opinion term with the maximum length from the candidate
sentiment snippet to output sentiment triplets. Furthermore, we propose a
simple but effective model based on the STAGE, which outperforms the
state-of-the-arts by a large margin on four widely-used datasets. Moreover, our
STAGE can be easily generalized to other pair/triplet extraction tasks, which
also demonstrates the superiority of the proposed scheme STAGE.
