Continual Adapter Tuning (CAT): A Parameter-Efficient Machine Learning Framework that Avoids Catastrophic Forgetting and Enables Knowledge Transfer from Learned ASC Tasks to New ASC Tasks

Aspect Sentiment Classification (ASC) is a critical task aimed at discerning sentiment polarity within specific domains, such as product reviews, where the sentiment toward particular aspects needs to be identified. Continual Learning (CL) poses a significant challenge for ASC models due to Catastrophic Forgetting (CF), wherein learning new tasks leads to a detrimental loss of […]

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