Aspect-based sentiment analysis (ABSA) is an NLP task that entails processing user-generated reviews to determine (i) the target being evaluated, (ii) the aspect category to which it belongs, and (iii) the sentiment expressed towards the target and aspect pair. In this article, we propose transforming ABSA into an abstract summary-like conditional text generation task that uses targets, aspects, and polarities to generate auxiliary statements. To demonstrate the efficacy of our task formulation and a proposed system, we fine-tune a pre-trained model for conditional text generation tasks to get new state-of-the-art results on a few restaurant domains and urban neighborhoods domain benchmark datasets.

翻译：以外观为基础的情绪分析(ABSA)是国家语言方案的一项任务,需要处理用户产生的审查,以确定(一) 评估的目标,(二) 其所属的方面类别,(三) 对目标和方对表示的情绪,在本条中,我们建议将ABSA转换为抽象的、类似于摘要的有条件文本生成任务,使用目标、方面和对数来生成辅助声明。为了展示我们的任务拟订和拟议系统的效力,我们微调了有条件文本生成任务预先培训的模式,以获得少数餐厅区和城市街区区域基准数据集的最新结果。