In this paper, we present InstructABSA, Aspect Based Sentiment Analysis (ABSA) using the instruction learning paradigm for the 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) the ABSA subtasks, yielding significant performance improvements. Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on the three ABSA subtasks (ATE, ATSC, and Joint Task) by a significant margin, outperforming 7x larger models. In particular, InstructABSA surpasses the SOTA on the Rest14 ATE subtask by 5.69% points, Rest15 ATSC subtask by 9.59% points, and on the Lapt14 Joint Task by 3.37% points. Our results also suggest a strong generalization ability to new domains across all three subtasks

翻译：本文提出InstructABSA，一种采用指令学习范式进行方面级情感分析（ABSA）的方法，针对其子任务：方面术语抽取（ATE）、方面术语情感分类（ATSC）以及联合任务建模。 我们的方法为每个训练样本引入正例、负例和中性示例，并通过指令微调模型（Tk-Instruct）处理ABSA子任务，从而显著提升性能。 在SemEval 2014、2015和2016数据集上的实验结果表明，InstructABSA在三个ABSA子任务（ATE、ATSC和联合任务）上均大幅超越先前的最优方法（SOTA），且性能优于规模大7倍的模型。 具体而言，InstructABSA在Rest14数据集的ATE子任务上超越SOTA 5.69个百分点，在Rest15数据集的ATSC子任务上超越9.59个百分点，在Lapt14数据集的联合任务上超越3.37个百分点。 我们的结果还表明，该方法在所有三个子任务上均展现出对新领域的强泛化能力。