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：本文提出InstructABSA，一种基于指令学习范式（instruction learning paradigm）的方面情感分析（ABSA）方法，覆盖所有ABSA子任务：方面词抽取（ATE）、方面词情感分类（ATSC）以及联合任务建模。该方法为每个训练样本引入正例、负例和中性示例，并针对各ABSA子任务对模型（Tk-Instruct Base）进行指令微调（instruction tuning），从而取得了显著的性能提升。在SemEval 2014数据集上的实验结果显示，InstructABSA在所有三个ABSA子任务（ATE、ATSC和联合任务）上均大幅超越此前的最优方法（SOTA），且性能优于规模大7倍的模型。具体而言，InstructABSA在餐厅ATE子任务上超越SOTA 7.31个百分点，在笔记本电脑联合任务上超越8.63个百分点。此外，实验结果还表明该方法在所有三个子任务上均展现出对未见任务的强大泛化能力。