  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.
