Source-linked AI summary
Continual Lifelong Learning in Natural Language Processing: A Survey
Magdalena Biesialska, Katarzyna Biesialska, Marta R. Costa-jussà
TL;DR
Continual learning in NLP must adapt to new tasks and changing data without forgetting prior knowledge, but the field remains nascent and difficult to evaluate consistently. This survey synthesizes CL methods across NLP tasks, reviews benchmarks and metrics, and identifies research gaps. It concludes that stronger evaluation settings and broader research are needed, especially for generalization beyond controlled scenarios.
Problem
NLP continual learning must retain previously learned knowledge while adapting to new information, but existing methods and evaluation resources remain limited.
Method
The survey classifies continual-learning methods, reviews their application across NLP tasks, and summarizes datasets, evaluation approaches, and research gaps.
Results
The survey finds no unified evaluation approach and a scarcity of datasets and benchmarks specifically designed for continual learning in NLP.
Takeaways & Limitations
Future progress requires challenging datasets and metrics that assess knowledge reuse, learning speed, forgetting, and generalization in NLP continual learning.
Takeaways & Limitations
Existing evaluation datasets often favor easy, controlled scenarios, while current methods are not robust or flexible enough for more complex conditions.
Abstract
from arXiv · showhide
Continual learning (CL) aims to enable information systems to learn from a continuous data stream across time. However, it is difficult for existing deep learning architectures to learn a new task without largely forgetting previously acquired knowledge. Furthermore, CL is particularly challenging for language learning, as natural language is ambiguous: it is discrete, compositional, and its meaning is context-dependent. In this work, we look at the problem of CL through the lens of various NLP tasks. Our survey discusses major challenges in CL and current methods applied in neural network models. We also provide a critical review of the existing CL evaluation methods and datasets in NLP. Finally, we present our outlook on future research directions.
1 Introduction
Continual learning in NLP addresses the difficulty of adapting to changing information while retaining previously acquired knowledge. The survey focuses on a still-nascent research area shaped by catastrophic forgetting and costly adaptation strategies.
- Existing neural networks learn largely in isolation and struggle to retain prior knowledge while adapting to new information.Changing data distributions, or concept drift, can also reduce model accuracy over time.
- Catastrophic forgetting makes retraining models from scratch for new tasks or distributions costly and time-consuming.Capacity saturation and model expansion further complicate continual adaptation.
- Continual learning in NLP remains nascent, with relatively few methods and evaluation benchmarks compared with other domains.The survey positions itself as one of the few studies summarizing continual and lifelong learning research in NLP.
2 Learning Paradigms
The survey defines continual learning as sequential adaptation across potentially changing tasks and distributions while reusing knowledge and avoiding forgetting. It relates CL to neighboring paradigms and organizes mitigation methods into rehearsal, regularization, architectural, and hybrid approaches, while noting practical departures from idealized desiderata.
- Continual Learning: Continual learning sequentially adapts across time by leveraging previous tasks to improve generalization on future tasks.Its formal objective combines learning the current task with minimizing catastrophic forgetting on earlier tasks.
- Continual Learning: Practical CL systems often relax desiderata by using shuffled batches, large labeled datasets, multiple data passes, or known task identities.These choices leave systems less exposed to few-shot, unsupervised, self-supervised, and unknown-distribution settings.
- Related Machine Learning Paradigms: CL shares knowledge-transfer principles with related paradigms, including positive backward transfer for old tasks and positive forward transfer for new tasks.Positive forward transfer can support learning a new task from few examples, while negative transfer impedes learning.
- Approaches to Continual Learning: The survey distinguishes rehearsal, regularization, architectural, and hybrid method families for mitigating catastrophic forgetting.Rehearsal retains prior examples, regularization constrains parameter changes, and architectural methods add or isolate task-specific parameters.
- Approaches to Continual Learning: Pseudo-rehearsal generates examples from estimated prior-task distributions instead of storing original training samples.FearNet and DGR are cited as representative generative approaches.
- Approaches to Continual Learning: Knowledge distillation transfers a teacher model’s predictions to a smaller student while keeping predictions for past tasks invariant.This approach is especially suitable for large neural machine translation models, where reducing model size is beneficial.
- Approaches to Continual Learning: Architectural methods prevent forgetting through modular changes, fixed or masked previous parameters, and dynamically added layers.Their main drawback is substantially increasing the number of parameters as new tasks are accommodated.
3 Evaluation
NLP continual-learning evaluation lacks unified benchmarks and metrics, with protocols spanning plasticity, stability, knowledge retention, and learning speed. Existing datasets and evaluation practices remain limited, motivating more challenging tests of generalization to unseen tasks.
- NLP continual learning lacks a unified evaluation approach, with scarce domain-specific datasets and benchmark schemes.
- 3.1 Protocols: Existing protocols commonly assess plasticity and stability, but many use small datasets or evaluate only a limited number of methods.
- 3.2 Benchmarks and Metrics: GLUE, SUPERGLUE, DECANLP, and XTREME provide multi-task NLP benchmarks using diverse tasks and task-specific metrics.
- 3.2 Benchmarks and Metrics: Evaluation should measure average performance, forgetting, prior-knowledge reuse, and the speed of learning new tasks; proposed metrics include Average Accuracy, Forgetting Measure, LCA, and online codelength.
- 3.3 Evaluation Datasets: Most established continual-learning datasets come from non-NLP domains, while NLP studies often adapt popular corpora or use narrow, domain-specific datasets.
- 3.3 Evaluation Datasets: As evaluation datasets become more complex, model performance often decreases, supporting calls for challenging datasets and metrics that test generalization to unseen tasks.
4 Continual Learning in NLP Tasks
Continual learning methods in NLP have been applied across representations, language models, question answering, sentiment analysis, translation, and other tasks. The surveyed approaches include domain adaptation, replay, regularization, distillation, curriculum learning, and multilingual or multi-task modeling.
- Word and Sentence Representations: Word and sentence representation methods address vocabulary and meaning shifts across time and domains while attempting to prevent catastrophic forgetting.Examples include meta-learning for new-domain embeddings and matrix conceptors for continually learning corpus-dependent features.
- Language Modeling: Pre-trained contextual language models support broad supervised NLP performance through inductive transfer and reuse of learned language representations.Typical systems pre-train a language model on unlabeled text and then adapt it to supervised downstream tasks.
- Question Answering: Continual learning for question answering spans reading comprehension, information extraction, dialogue systems, and never-ending knowledge accumulation.Reported approaches include episodic memory with sparse replay and local adaptation, online learning from human interactions, and embedding alignment for relation extraction.
- Sentiment Analysis and Text Classification: Sentiment-analysis research targets adaptation to new domains and improved discovery of domain-specific word polarities, opinion targets, and aspects.Approaches include unsupervised continual learning for opinion-target classification and conditional random fields for supervised aspect extraction across time.
- Neural Machine Translation: Neural machine translation has been adapted through continued training, multilingual modeling, curriculum learning, regularization, and knowledge distillation.These methods address domain or language changes using small in-domain corpora, sample-selection strategies, parameter regularization, and teacher-student matching.
5 Research Gaps and Future Directions
The survey identifies limited research coverage and constraints in data, architectures, and hardware as major gaps for continual learning in NLP. It calls for better handling of realistic data streams, more efficient and adaptive models, and comprehensive evaluation benchmarks.
- Research Gaps: Current continual-learning research in NLP remains scant, and existing methods are limited across data, model architectures, and hardware.The survey expects task-agnostic approaches to become more prevalent than the currently dominant task-specific methods.
- Data Challenges: Real-world continual learning must handle partial information, non-i.i.d. data, interventions, environmental changes, few-shot learning, and out-of-distribution generalization.Sequence-to-sequence models also struggle with systematic generalization and learning general rules or high-level language concepts.
- Model and Hardware Directions: Combining continual learning with Bayesian principles, compression, and meta-learning could improve parameter management, reduce computational demands, and support adaptation from limited samples.Suggested directions include pruning, quantization, knowledge distillation, low-rank factorization, and causality-informed replay.
- Evaluation: Comprehensive NLP continual-learning benchmarks should complement multi-task benchmarks and use evaluation schemes beyond traditional accuracy and F1 metrics.The survey calls for clearly defined data and task boundaries together with more robust evaluation approaches.
6 Conclusion
The survey provides a comprehensive overview of continual learning in NLP, covering methods, applications, datasets, evaluation approaches, research gaps, and future directions.
- Conclusion: The survey classifies machine-learning paradigms and methods for alleviating catastrophic forgetting, relates them to NLP tasks, summarizes benchmarks and evaluation approaches, and identifies future research directions.It aims to encourage further research and a more holistic view of linguistic intelligence.