  Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms,
their corresponding sentiment polarities, and the opinion terms. There exist
seven subtasks in ABSA. Most studies only focus on the subsets of these
subtasks, which leads to various complicated ABSA models while hard to solve
these subtasks in a unified framework. In this paper, we redefine every subtask
target as a sequence mixed by pointer indexes and sentiment class indexes,
which converts all ABSA subtasks into a unified generative formulation. Based
on the unified formulation, we exploit the pre-training sequence-to-sequence
model BART to solve all ABSA subtasks in an end-to-end framework. Extensive
experiments on four ABSA datasets for seven subtasks demonstrate that our
framework achieves substantial performance gain and provides a real unified
end-to-end solution for the whole ABSA subtasks, which could benefit multiple
tasks.
