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，采用指令学习范式统一处理所有方面级情感分析子任务：方面词提取（ATE）、方面词情感分类（ATSC）及联合任务建模。该方法为每个训练样本引入正向、负向和中性示例，并通过指令微调模型（Tk-Instruct Base）以适配各子任务，显著提升性能。在SemEval 2014数据集上的实验表明，InstructABSA在所有三个ABSA子任务（ATE、ATSC及联合任务）上均大幅超越先前最优方法（SOTA），且性能优于规模大7倍的模型。特别地，InstructABSA在餐厅ATE子任务上以7.31%的绝对优势超越SOTA，在笔记本电脑联合任务上则以8.63%的绝对优势领先。此外，实验结果还揭示了该方法在所有三个子任务中具备对未见任务的强泛化能力。