  Aspect-based sentiment analysis (ABSA) has been extensively studied in recent
years, which typically involves four fundamental sentiment elements, including
the aspect category, aspect term, opinion term, and sentiment polarity.
Existing studies usually consider the detection of partial sentiment elements,
instead of predicting the four elements in one shot. In this work, we introduce
the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all
sentiment elements in quads for a given opinionated sentence, which can reveal
a more comprehensive and complete aspect-level sentiment structure. We further
propose a novel \textsc{Paraphrase} modeling paradigm to cast the ASQP task to
a paraphrase generation process. On one hand, the generation formulation allows
solving ASQP in an end-to-end manner, alleviating the potential error
propagation in the pipeline solution. On the other hand, the semantics of the
sentiment elements can be fully exploited by learning to generate them in the
natural language form. Extensive experiments on benchmark datasets show the
superiority of our proposed method and the capacity of cross-task transfer with
the proposed unified \textsc{Paraphrase} modeling framework.
