Continual Lifelong Learning in Natural Language Processing: A Survey
The field of NLP is increasingly confronting the need for models that can learn new information continually while retaining previously acquired knowledge. This paper, authored by Magdalena Biesialska, Katarzyna Biesialska, and Marta R. Costa-jussà , provides a comprehensive survey of continual lifelong learning (CL) in NLP, articulating the challenges, solutions, and research directions associated with this domain.
Continual learning entails adapting to new tasks while maintaining proficiency in previous ones, a challenge known as catastrophic forgetting (CF). This problem is profound in NLP due to the discrete, compositional, and context-dependent characteristics of language. Current methodologies are constrained by concept drift, dynamic environmental changes, and non-i.i.d. data distributions, necessitating solutions that extend beyond static task confines.
Challenges and Methodologies
Continual learning faces several challenges, including:
- Catastrophic Forgetting: As models learn new tasks, they tend to forget previously learned ones.
- Stability-Plasticity Dilemma: Balancing the model's ability to retain old knowledge (stability) with its capacity to learn new tasks (plasticity).
- Memory Constraints: Efficient use of limited memory to retain knowledge of past tasks.
Approaches to address CF are categorized primarily into rehearsal, regularization, and architectural methods. Rehearsal strategies involve retraining on data from previous tasks, whereas regularization methods introduce penalty terms to mitigate weight changes significant for older tasks. Architectural changes involve modifying model infrastructure to empower task-specific parameters.
Evaluation Protocols
The paper critiques the lack of standardized evaluation metrics and benchmarks in CL applied to NLP, pointing out that much of the work borrows benchmarks from domains like computer vision. Protocols to evaluate CL systems should not only assess accuracy but also the speed of adaptation to new tasks and the degree of knowledge retention for previously seen tasks. Key metrics such as Average Accuracy, Forgetting Measure, and Learning Curve Area offer nuanced insights into these dimensions.
Application Across NLP Tasks
The paper explores the application of CL across several NLP tasks such as:
- Word and Sentence Representations: Addressing shifts in meaning over time and domain by leveraging domain-specific continuous learning of embeddings.
- LLMing: Utilizing pre-trained LLMs like BERT for task and domain adaptation, thus preventing CF without extensive retraining.
- Question Answering and Dialogue Systems: Continuous learning from interactions is vital to enhance capabilities in understanding and generating natural language.
- Sentiment Analysis and Text Classification: Addressing domain adaptation challenges by building models resilient to shifting opinion dynamics.
Future Research Trajectories
Emerging research directions emphasize the need for task-agnostic CL methods and innovative solutions across data, models, and evaluation criteria:
- Data Handling: Models must learn efficiently from sparse data and perform well across non-i.i.d. distributions. They should incorporate causal reasoning to enhance adaptability.
- Model Efficiency: Lightweight models, potentially through techniques like knowledge distillation and parameter pruning, are key to achieving scalability in continual learning settings.
- Benchmark Development: The establishment of NLP-specific CL benchmarks would set a standard for measuring progress and push the boundary of linguistic intelligence.
Conclusion
The survey underscores the nascent yet rapidly evolving landscape of CL in NLP, highlighting critical gaps and opportunities. By fostering development along the aforementioned lines, the field can aspire to models capable of robust, real-world language understanding, continually learning and adapting akin to human cognitive capabilities. This work serves as a pivotal guide for researchers aiming to advance the state of NLP through CL methodologies.