Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis
task which involves four elements from user-generated texts: aspect term,
aspect category, opinion term, and sentiment polarity. Most computational
approaches focus on some of the ABSA sub-tasks such as tuple (aspect term,
sentiment polarity) or triplet (aspect term, opinion term, sentiment polarity)
extraction using either pipeline or joint modeling approaches. Recently,
generative approaches have been proposed to extract all four elements as (one
or more) quadruplets from text as a single task. In this work, we take a step
further and propose a unified framework for solving ABSA, and the associated
sub-tasks to improve the performance in few-shot scenarios. To this end, we
fine-tune a T5 model with instructional prompts in a multi-task learning
fashion covering all the sub-tasks, as well as the entire quadruple prediction
task. In experiments with multiple benchmark data sets, we show that the
proposed multi-task prompting approach brings performance boost (by absolute
$6.75$ F1) in the few-shot learning setting.

本文提出了一个统一的框架来解决基于方面的情感分析（ABSA）及其相关子任务，通过在多任务学习模式下使用 T5 模型，通过教学提示进行 fine-tuning，并在多个基准数据集上实现了表现提升（F1 绝对值增加了 6.75），特别是在少样本情况下。