Papers › Continual Learning with Knowledge Transfer for Sentiment Classification

Continual Learning with Knowledge Transfer for Sentiment Classification

18 Dec 2021arXiv:2112.10021archive 2025-07-28

Zixuan Ke, Bing Liu, Hao Wang, Lei Shu

This paper studies continual learning (CL) for sentiment classification (SC). In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain. Two natural questions are: Can the system transfer the knowledge learned in the past from the previous tasks to the new task to help it learn a better model for the new task? And, can old models for previous tasks be improved in the process as well? This paper proposes a novel technique called KAN to achieve these objectives. KAN can markedly improve the SC accuracy of both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of KAN is demonstrated through extensive experiments.

Paper PDF Code

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

Code

zixuanke/lifelongsentclass officialmentioned in paperpytorch report

zixuanke/pycontinual officialpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Classification Continual Learning Natural Questions Sentiment Analysis Sentiment Classification Transfer Learning

Datasets

Introduced by this paper, per the archive.

DSC (10 tasks)

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Continual Learning	ASC (19 tasks)	KAN	F1 - macro	0.7738	#9 of 15	Archive leaderboard	report
Continual Learning	DSC (10 tasks)	KAN	F1 - macro	0.8123	#4 of 6	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections