In this paper, we present InstructABSA, Aspect-Based Sentiment Analysis (ABSA) using instruction learning paradigm for all ABSA subtasks: Aspect Term Extraction (ATE), Aspect Term Sentiment Classification (ATSC), and Joint Task modeling. Our method introduces positive, negative, and neutral examples to each training sample, and instruction tunes the model (Tk-Instruct Base) for each ABSA subtask, yielding significant performance improvements. Experimental results on the Sem Eval 2014 dataset demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on all three ABSA subtasks (ATE, ATSC, and Joint Task) by a significant margin, outperforming 7x larger models. In particular, InstructABSA surpasses the SOTA on the restaurant ATE subtask by 7.31% points and on the Laptop Joint Task by 8.63% points. Our results also suggest a strong generalization ability to unseen tasks across all three subtasks.

翻译：在本文中,我们介绍教官ABSA, 使用所有ABSA子任务的指导学习范式, 光谱感应分析(ABSA) 使用所有ABSA子任务的指导学习范式: 外观抽取(ATE) 、 外观感应(ATSC) 和联合任务模型。 我们的方法为每个培训样本引入了正、负和中性的例子, 并给ABSA的每个子任务调整了模型( Tk- Instruct Base), 取得了显著的性能改进。 Sem Eval 2014 数据集的实验结果表明, 教官ABSA 在所有三个亚类任务( ATE、 ATSC 和联合任务) 上, 都以显著的差幅超过7x大模型, 特别是, 指示ABSA 超过餐厅 ATE 子任务SATA的7.31%点和笔托联合任务的8.63%点。 我们的结果还表明, 在所有三个子任务中, 都具有很强的隐形任务一般化能力。