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，这是一种使用指令学习范式的Aspect Based Sentiment Analysis (ABSA)方法，用于处理ABSA子任务：Aspect Term Extraction (ATE)、Aspect Term Sentiment Classification (ATSC)和Joint Task建模。我们的方法将正向、负向和中性示例引入到每个训练样本中，并使用指令调节模型 (T K-Instruct) 进行ABSA子任务的处理，从而显著提高了模型的性能。在Sem Eval 2014、15和16数据集上的实验结果表明，InstructABSA在三个ABSA子任务（ATE、ATSC和Joint Task）上都显著优于之前的最先进方法 (SOTA)，超越了7倍大小的模型。特别是，在Rest14 ATE子任务上，InstructABSA超过SOTA 5.69个百分点；在Rest15 ATSC子任务上，超过SOTA 9.59个百分点；在Lapt14 Joint Task上超过SOTA 3.37个百分点。我们的结果还表明，在三个子任务中，在新领域中具有很强的泛化能力。