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InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis.

NAACL (Short Papers)(2024)

Cited 58|Views151
Abstract
We introduce InstructABSA, an instruction learning paradigm for Aspect-Based Sentiment Analysis (ABSA) subtasks. Our method introduces positive, negative, and neutral examples to each training sample, and instruction tune the model (Tk-Instruct) for 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 Term Extraction (ATE), Sentiment Classification(ATSC) and Sentiment Pair Extraction (ASPE) subtasks. In particular, InstructABSA outperforms the previous state-of-the-art (SOTA) on the Rest14 ATE subtask by 5.69% points, the Rest15 ATSC subtask by 9.59% points, and the Lapt14 AOPE subtask by 3.37% points, surpassing 7x larger models. We also get competitive results on AOOE, AOPE, and AOSTE subtasks indicating strong generalization ability to all subtasks. Exploring sample efficiency reveals that just 50% train data is required to get competitive results with other instruction tuning approaches. Lastly, we assess the quality of instructions and observe that InstructABSA's performance experiences a decline of ~10% when adding misleading examples.
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Aspect-based Sentiment Analysis,Sentiment Analysis,Emotion Recognition,Multi-label Learning
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要点】:本文提出了一种新的指令学习范式InstructABSA,通过引入正、负、中性示例及指令调优模型,显著提升了方面情感分析子任务的性能。

方法】:InstructABSA方法为每个训练样本引入正、负、中性示例,并通过指令调优(Tk-Instruct)模型以适应方面情感分析(ABSA)子任务。

实验】:在SemEval 2014、15、16数据集上进行实验,InstructABSA在方面提取(ATE)、情感分类(ATSC)和情感对提取(ASPE)子任务上均超越了之前的最先进方法,并在某些子任务上超过7倍规模更大的模型。特别地,InstructABSA在Rest14 ATE子任务上提高了5.69%,在Rest15 ATSC子任务上提高了9.59%,在Lapt14 AOPE子任务上提高了3.37%。此外,该方法在AOOE、AOPE和AOSTE子任务上也取得了有竞争力的结果,显示了强大的泛化能力。研究样本效率发现,仅使用50%的训练数据即可与其他指令调优方法获得竞争力。最后,评估指令质量时发现,加入误导性示例后InstructABSA的性能下降了约10%。