Unified ABSA via Annotation-Decoupled Multi-task Instruction Tuning

Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. Different ABSA tasks are designed for different real-world applications. However, application scenarios of ABSA tasks are often diverse, typically requiring training separate systems for each task on the task-specific labeled data and making separate predictions. Secondly, different tasks often contain shared sentiment elements. Training task-specific models either fail to exploit the shared knowledge among multiple ABSA tasks effectively or neglect the complementarity between tasks. Thirdly, despite the existence of the compound ABSA task such as quadruple extraction and triple extraction, it is not easy to obtain satisfactory performance due to the coupling of multiple elements. To tackle these issues, we present UNIFIEDABSA, a general-purpose ABSA framework based on multi-task instruction tuning, aiming at "one-model-for-all-tasks". We also introduce a new annotation-decoupled multi-task learning mechanism that only depends on annotation on the compound task rather than all tasks. This mechanism not only fully utilizes the existing annotations from the compound task, but also alleviates the complicated coupling relationship among multiple elements, making the learning more effective. Extensive experiments show that UNIFIEDABSA, can consistently outperform dedicated models in fully-supervised and low-resource settings for almost all 11 ABSA tasks. We also conduct further experiments to demonstrate the general applicability of our framework.