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Continual Adapter Tuning (CAT): A Parameter-Efficient Machine Learning Framework
Addressing the Challenge of Catastrophic Forgetting in Aspect Sentiment Classification
Aspect Sentiment Classification (ASC) involves identifying sentiment polarity within specific domains, such as product reviews. However, Continual Learning (CL) presents a challenge due to Catastrophic Forgetting (CF) when learning new tasks leads to loss of previously acquired knowledge.
Traditional techniques struggle to handle an increasing number of tasks effectively. Recent methods aim to reduce CF by freezing the core model and training task-specific components. However, they often fail to facilitate effective knowledge transfer between tasks.
A new research approach, Continual Adapter Tuning (CAT), addresses these limitations by employing task-specific adapters while freezing the backbone pre-trained model. This prevents catastrophic forgetting and enables efficient learning of new tasks. CAT also utilizes continual adapter initialization and label-aware contrastive learning to enhance sentiment polarity classification, resulting in a parameter-efficient framework that improves ASC performance.
The CAT framework leverages Adapter-BERT architecture, a variant of the BERT model, to ensure efficient and accurate sentiment polarity classification in ASC tasks while supporting continual learning and knowledge transfer.
Evaluation and Effectiveness
The authors evaluated the CAT framework through experiments comparing it with various baselines across 19 ASC datasets, demonstrating its superiority in accuracy and Macro-F1 metrics. Ablation studies and parameter efficiency comparisons further validated CAT’s effectiveness.
Practical Implementation and Future Research
The CAT framework offers a straightforward yet highly effective parameter-efficient solution for continual aspect sentiment classification within a domain-incremental learning context. Its applicability beyond domain-incremental learning settings is a potential area for future research.
If you want to evolve your company with AI, consider leveraging Continual Adapter Tuning (CAT) to stay competitive and redefine your way of work.
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