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Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grainedaspect-level sentiment information. There are many ABSA tasks, and the currentdominant paradigm is to train task-specific models for each task. However,application scenarios of ABSA tasks are often diverse. This solution usuallyrequires 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 presentUnifiedABSA, a general-purpose ABSA framework based on multi-task instructiontuning, which can uniformly model various tasks and capture the inter-taskdependency with multi-task learning. Extensive experiments on two benchmarkdatasets show that UnifiedABSA can significantly outperform dedicated models on11 ABSA tasks and show its superiority in terms of data efficiency.

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