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.

本文旨在通过将每个 ABSA 子任务目标重新定义为由指针索引和情感类别索引混合的序列，将 ABSA 的各个子任务转换为统一的生成式公式，并利用预训练序列到序列模型 BART 在端到端框架中解决所有 ABSA 子任务。实验结果表明，我们的框架在四个 ABSA 数据集上实现了实质性的性能收益，并为整个 ABSA 子任务提供了真正的统一端到端解决方案，这可使多个任务受益。