{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adapting-bert-for-continual-learning-of-a-1","title":"Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks","arxiv_id":"2112.03271","date":"2021-12-06","proceeding":"NAACL 2021 4","authors":["Zixuan Ke","Hu Xu","Bing Liu"],"abstract":"This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks. Although some CL techniques have been proposed for document sentiment classification, we are not aware of any CL work on ASC. A CL system that incrementally learns a sequence of ASC tasks should address the following two issues: (1) transfer knowledge learned from previous tasks to the new task to help it learn a better model, and (2) maintain the performance of the models for previous tasks so that they are not forgotten. This paper proposes a novel capsule network based model called B-CL to address these issues. B-CL markedly improves the ASC performance on both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of B-CL is demonstrated through extensive experiments.","url_abs":"https://arxiv.org/abs/2112.03271v1","url_pdf":"https://arxiv.org/pdf/2112.03271v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adapting-bert-for-continual-learning-of-a-1","repo_url":"https://github.com/zixuanke/pycontinual","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"aware","method_name":"AWARE"},{"method_slug":"fixcaps","method_name":"Capsule Network"}],"datasets_introduced":[{"slug":"asc-til-19-tasks","name":"ASC (TIL, 19 tasks)","full_name":"Task Incremental Aspect Sentiment Classification"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/continual-learning-on-20newsgroup-10-tasks","task":"Continual Learning","dataset":"20Newsgroup (10 tasks)","model":"B-CL","rank_in_archive_order":4,"of":6,"metrics":{"F1 - macro":"0.9504"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-asc-19-tasks","task":"Continual Learning","dataset":"ASC (19 tasks)","model":"B-CL","rank_in_archive_order":3,"of":15,"metrics":{"F1 - macro":"0.8140"},"uses_additional_data":false},{"leaderboard":"/sota/continual-learning-on-dsc-10-tasks","task":"Continual Learning","dataset":"DSC (10 tasks)","model":"B-CL","rank_in_archive_order":5,"of":6,"metrics":{"F1 - macro":"0.7651"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2112.03271","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}