  Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term
(aspect), sentiment and opinion term (opinion) triplets from sentences and can
tell a complete story, i.e., the discussed aspect, the sentiment toward the
aspect, and the cause of the sentiment. ASTE is a charming task, however, one
triplet extracted by ASTE only includes one opinion of the aspect, but an
aspect in a sentence may have multiple corresponding opinions and one opinion
only provides part of the reason why the aspect has this sentiment, as a
consequence, some triplets extracted by ASTE are hard to understand, and
provide erroneous information for downstream tasks. In this paper, we introduce
a new task, named Aspect Sentiment Multiple Opinions Triplet Extraction
(ASMOTE). ASMOTE aims to extract aspect, sentiment and multiple opinions
triplets. Specifically, one triplet extracted by ASMOTE contains all opinions
about the aspect and can tell the exact reason that the aspect has the
sentiment. We propose an Aspect-Guided Framework (AGF) to address this task.
AGF first extracts aspects, then predicts their opinions and sentiments.
Moreover, with the help of the proposed Sequence Labeling Attention(SLA), AGF
improves the performance of the sentiment classification using the extracted
opinions. Experimental results on multiple datasets demonstrate the
effectiveness of our approach.
