# CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks Source: [CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:50:06.694Z Authors: Zixuan Ke, Bing Liu, Hu Xu, Lei Shu Published: 2021-12-05T23:55:53.000Z ## Abstract This paper studies continual learning (CL) of a sequence of aspect sentiment classification(ASC) tasks in a particular CL setting called domain incremental learning (DIL). Each task is from a different domain or product. The DIL setting is particularly suited to ASC because in testing the system needs not know the task/domain to which the test data belongs. To our knowledge, this setting has not been studied before for ASC. This paper proposes a novel model called CLASSIC. The key novelty is a contrastive continual learning method that enables both knowledge transfer across tasks and knowledge distillation from old tasks to the new task, which eliminates the need for task ids in testing. Experimental results show the high effectiveness of CLASSIC. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [End-task Classification on Continual DAP Domain Suite Restaurant, ACL, AI, Phone, Camera, PubMed](): End-task Classification; Continual DAP Domain Suite Restaurant, ACL, AI, Phone, Camera, PubMed; Restaurant Macro F1: 0.7989. ## Original sources and implementation [Original paper]() [PDF]()