# Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

arXiv: https://arxiv.org/abs/2112.03271 · HTML: https://mlsanity.com/paper/2112.03271/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 51 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 30 of 32)

- [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)
- [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)
- [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)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [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)
- [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)
- [What Happens To BERT Embeddings During Fine-tuning?](https://mlsanity.com/paper/2004.14448/) (2020)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016)
- [Aspect Based Sentiment Analysis with Gated Convolutional Networks](https://mlsanity.com/paper/1805.07043/) (2018)
- [Deep Generative Dual Memory Network for Continual Learning](https://mlsanity.com/paper/1710.10368/) (2017)
- [Continual Learning in Generative Adversarial Nets](https://mlsanity.com/paper/1705.08395/) (2017)
- [Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay](https://mlsanity.com/paper/1903.04566/) (2019)
- [Lifelong Learning for Sentiment Classification](https://mlsanity.com/paper/1801.02808/) (2018)
- [Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications](https://mlsanity.com/paper/1906.02829/) (2019)
- [Lifelong Learning CRF for Supervised Aspect Extraction](https://mlsanity.com/paper/1705.00251/) (2017)

## Built on by (most cited first, 30 of 46)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Continual Pre-training of Language Models](https://mlsanity.com/paper/2302.03241/) (2023)
- [Fine-tuned Language Models are Continual Learners](https://mlsanity.com/paper/2205.12393/) (2022)
- [A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning](https://mlsanity.com/paper/2205.13218/) (2022)
- [Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time](https://mlsanity.com/paper/2211.14238/) (2022)
- [Continual Learning of Natural Language Processing Tasks: A Survey](https://mlsanity.com/paper/2211.12701/) (2022)
- [Modular Deep Learning](https://mlsanity.com/paper/2302.11529/) (2023)
- [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)
- [Memory Efficient Continual Learning with Transformers](https://mlsanity.com/paper/2203.04640/) (2022)
- [Continual Training of Language Models for Few-Shot Learning](https://mlsanity.com/paper/2210.05549/) (2022)
- [Continual Sequence Generation with Adaptive Compositional Modules](https://mlsanity.com/paper/2203.10652/) (2022)
- [Meta-attention for ViT-backed Continual Learning](https://mlsanity.com/paper/2203.11684/) (2022)
- [ConTinTin: Continual Learning from Task Instructions](https://mlsanity.com/paper/2203.08512/) (2022)
- [Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection](https://mlsanity.com/paper/2204.07275/) (2022)
- [Unlocking Continual Learning Abilities in Language Models](https://mlsanity.com/paper/2406.17245/) (2024)
- [Class Incremental Learning via Likelihood Ratio Based Task Prediction](https://mlsanity.com/paper/2309.15048/) (2023)
- [Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language Models](https://mlsanity.com/paper/2311.08106/) (2023)
- [Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuning](https://mlsanity.com/paper/2203.11720/) (2022)
- [Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2405.05496/) (2024)
- [ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks](https://mlsanity.com/paper/2405.14211/) (2024)
- [Parameter-Efficient Finetuning for Robust Continual Multilingual Learning](https://mlsanity.com/paper/2209.06767/) (2022)
- [Teamwork Is Not Always Good: An Empirical Study of Classifier Drift in Class-incremental Information Extraction](https://mlsanity.com/paper/2305.16559/) (2023)
- [Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt](https://mlsanity.com/paper/2310.13024/) (2023)
- [Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks](https://mlsanity.com/paper/2310.09436/) (2023)
- [Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models](https://mlsanity.com/paper/2312.08888/) (2023)
- [In-context Continual Learning Assisted by an External Continual Learner](https://mlsanity.com/paper/2412.15563/) (2024)
- [Feature Forgetting in Continual Representation Learning](https://mlsanity.com/paper/2205.13359/) (2022)
- [Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition](https://mlsanity.com/paper/2310.10184/) (2023)

## Abstract (arXiv)

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.