UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning

• URL: http://arxiv.org/abs/2211.10986v1
• Date: Sun, 20 Nov 2022 14:21:09 GMT
• Title: UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning
• Authors: Zengzhi Wang, Rui Xia, Jianfei Yu
• Abstract summary: Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train task-specific models for each task. We present UnifiedABSA, a general-purpose ABSA framework based on multi-task instruction tuning.
• Score: 25.482853330324748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train task-specific models for each task. However, application scenarios of ABSA tasks are often diverse. This solution usually requires a large amount of labeled data from each task to perform excellently. These dedicated models are separately trained and separately predicted, ignoring the relationship between tasks. To tackle these issues, we present UnifiedABSA, a general-purpose ABSA framework based on multi-task instruction tuning, which can uniformly model various tasks and capture the inter-task dependency with multi-task learning. Extensive experiments on two benchmark datasets show that UnifiedABSA can significantly outperform dedicated models on 11 ABSA tasks and show its superiority in terms of data efficiency.

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