InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis

• URL: http://arxiv.org/abs/2302.08624v6
• Date: Mon, 13 Nov 2023 17:56:19 GMT
• Title: InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis
• Authors: Kevin Scaria and Himanshu Gupta and Siddharth Goyal and Saurabh Arjun Sawant and Swaroop Mishra and Chitta Baral
• Abstract summary: InstructABSA is 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.
• Score: 58.188050006989144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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