# CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks

arXiv: https://arxiv.org/abs/2112.02714 · HTML: https://mlsanity.com/paper/2112.02714/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 68 references that are in the map.
- First posted on arXiv 5 Dec 2021.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Contrastive Representation Distillation](https://mlsanity.com/paper/1910.10699/) (2019)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Continual learning with hypernetworks](https://mlsanity.com/paper/1906.00695/) (2019)
- [Overcoming Catastrophic Forgetting by Incremental Moment Matching](https://mlsanity.com/paper/1703.08475/) (2017)
- [Continual Learning of Context-dependent Processing in Neural Networks](https://mlsanity.com/paper/1810.01256/) (2018)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019)
- [LAMOL: LAnguage MOdeling for Lifelong Language Learning](https://mlsanity.com/paper/1909.03329/) (2019)
- [Uncertainty-based Continual Learning with Adaptive Regularization](https://mlsanity.com/paper/1905.11614/) (2019)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [A continual learning survey: Defying forgetting in classification tasks](https://mlsanity.com/paper/1909.08383/) (2019)
- [Conditional Channel Gated Networks for Task-Aware Continual Learning](https://mlsanity.com/paper/2004.00070/) (2020)

## Built on by (most cited first, 29 of 29)

- [Continual Pre-training of Language Models](https://mlsanity.com/paper/2302.03241/) (2023)
- [Continual Learning of Natural Language Processing Tasks: A Survey](https://mlsanity.com/paper/2211.12701/) (2022)
- [Towards Lifelong Learning of Large Language Models: A Survey](https://mlsanity.com/paper/2406.06391/) (2024)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Recent Advances of Foundation Language Models-based Continual Learning: A Survey](https://mlsanity.com/paper/2405.18653/) (2024)
- [Continual Training of Language Models for Few-Shot Learning](https://mlsanity.com/paper/2210.05549/) (2022)
- [Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection](https://mlsanity.com/paper/2204.07275/) (2022)
- [Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions](https://mlsanity.com/paper/2211.03524/) (2022)
- [Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning](https://mlsanity.com/paper/2405.11067/) (2024)
- [Class-Incremental Learning based on Label Generation](https://mlsanity.com/paper/2306.12619/) (2023)
- [Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2405.05496/) (2024)
- [CP-Prompt: Composition-Based Cross-modal Prompting for Domain-Incremental Continual Learning](https://mlsanity.com/paper/2407.21043/) (2024)
- [Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction](https://mlsanity.com/paper/2305.16559/) (2023)
- [Continual Predictive Learning from Videos](https://mlsanity.com/paper/2204.05624/) (2022)
- [Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt](https://mlsanity.com/paper/2310.13024/) (2023)
- [InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective](https://mlsanity.com/paper/2310.06362/) (2023)
- [CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.00791/) (2022)
- [Self-Evolving LLMs via Continual Instruction Tuning](https://mlsanity.com/paper/2509.18133/) (2025)
- [CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2310.15577/) (2023)
- [HOP to the Next Tasks and Domains for Continual Learning in NLP](https://mlsanity.com/paper/2402.18449/) (2024)
- [Continual Few-shot Event Detection via Hierarchical Augmentation Networks](https://mlsanity.com/paper/2403.17733/) (2024)
- [Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt Tuning](https://mlsanity.com/paper/2406.12251/) (2024)
- [IRS: Incremental Relationship-guided Segmentation for Digital Pathology](https://mlsanity.com/paper/2505.22855/) (2025)
- [DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning](https://mlsanity.com/paper/2507.02302/) (2025)
- [DIRA: Dynamic Domain Incremental Regularised Adaptation](https://mlsanity.com/paper/2205.00147/) (2022)
- [A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach](https://mlsanity.com/paper/2212.10051/) (2022)
- [Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions](https://mlsanity.com/paper/2305.18953/) (2023)
- [Exploring Kolmogorov-Arnold Network Expansions in Vision Transformers for Mitigating Catastrophic Forgetting in Continual Learning](https://mlsanity.com/paper/2507.04020/) (2025)
- [A Chinese Spelling Check Framework Based on Reverse Contrastive Learning](https://mlsanity.com/paper/2210.13823/) (2022)

## Abstract (arXiv)

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.