A Comparative Empirical Study of Catastrophic Forgetting Mitigation in Sequential Task Adaptation for Continual Natural Language Processing Systems
arXiv:2603.18641v1 Announce Type: new Abstract: Neural language models deployed in real-world applications must continually adapt to new tasks and domains without forgetting previously acquired knowledge. This work presents a comparative empirical study of catastrophic forgetting mitigation in continual intent classification. Using the CLINC150 dataset, we construct a 10-task label-disjoint scenario and evaluate three backbone architectures: a feed-forward Artificial Neural Network (ANN), a Gated Recurrent Unit (GRU), and a Transformer encoder, under a range of continual learning (CL) strategies. We consider one representative method from each major CL family: replay-based Maximally Interfered Retrieval (MIR), regularization-based Learning without Forgetting (LwF), and parameter-isolation via Hard Attention to Task (HAT), both individually and in all pairwise and triple combinations. Performance is assessed with average accuracy, macro F1, and backward transfer, capturing the stabilit
arXiv:2603.18641v1 Announce Type: new Abstract: Neural language models deployed in real-world applications must continually adapt to new tasks and domains without forgetting previously acquired knowledge. This work presents a comparative empirical study of catastrophic forgetting mitigation in continual intent classification. Using the CLINC150 dataset, we construct a 10-task label-disjoint scenario and evaluate three backbone architectures: a feed-forward Artificial Neural Network (ANN), a Gated Recurrent Unit (GRU), and a Transformer encoder, under a range of continual learning (CL) strategies. We consider one representative method from each major CL family: replay-based Maximally Interfered Retrieval (MIR), regularization-based Learning without Forgetting (LwF), and parameter-isolation via Hard Attention to Task (HAT), both individually and in all pairwise and triple combinations. Performance is assessed with average accuracy, macro F1, and backward transfer, capturing the stability-plasticity trade-off across the task sequence. Our results show that naive sequential fine-tuning suffers from severe forgetting for all architectures and that no single CL method fully prevents it. Replay emerges as a key ingredient: MIR is the most reliable individual strategy, and combinations that include replay (MIR+HAT, MIR+LwF, MIR+LwF+HAT) consistently achieve high final performance with near-zero or mildly positive backward transfer. The optimal configuration is architecture-dependent. MIR+HAT yields the best result for ANN and Transformer, MIR+LwF+HAT, on the other hand, works the best for GRU, and in several cases CL methods even surpass joint training, indicating a regularization effect. These findings highlight the importance of jointly selecting backbone architecture and CL mechanism when designing continual intent-classification systems.
Executive Summary
This comparative empirical study examines the effectiveness of various catastrophic forgetting mitigation strategies in continual intent classification for neural language models. Employing the CLINC150 dataset and three backbone architectures (ANN, GRU, and Transformer), the researchers evaluate the performance of replay-based MIR, regularization-based LwF, and parameter-isolation via HAT, both individually and in combinations across the task sequence. The findings indicate that replay emerges as a key ingredient, with MIR being the most reliable individual strategy. Combinations of MIR with other methods achieve high final performance with minimal forgetting. The optimal configuration is found to be architecture-dependent, with different combinations yielding the best results for each backbone. These results highlight the importance of jointly selecting backbone architecture and CL mechanism when designing continual intent-classification systems.
Key Points
- ▸ Catastrophic forgetting mitigation strategies can be categorized into three main families: replay-based, regularization-based, and parameter-isolation via hard attention.
- ▸ Replay-based MIR emerges as a key ingredient in preventing catastrophic forgetting.
- ▸ Combinations of MIR with other methods achieve high final performance with minimal forgetting, with the optimal configuration being architecture-dependent.
Merits
Strength of Empirical Evaluation
The study employs a comprehensive comparative empirical evaluation of various catastrophic forgetting mitigation strategies, using a diverse dataset and three backbone architectures, providing robust insights into the effectiveness of each approach.
Value of Architecture-Agnostic Approach
The study emphasizes the importance of jointly selecting backbone architecture and CL mechanism, highlighting the potential for optimal performance to depend on the specific architecture used.
Demerits
Limitation of Dataset
The study's findings are based on a single dataset (CLINC150), which may not generalize to other datasets or scenarios, limiting the scope of the study's conclusions.
Oversimplification of CL Mechanisms
The study evaluates a limited set of CL mechanisms, potentially oversimplifying the complexity of catastrophic forgetting prevention in real-world applications.
Expert Commentary
The study presents a comprehensive evaluation of catastrophic forgetting mitigation strategies in continual intent classification. While the findings are promising, they are limited by the use of a single dataset and the oversimplification of CL mechanisms. Future studies should aim to generalize the findings to other datasets and explore more complex CL mechanisms. The optimal configuration of CL mechanism and backbone architecture is indeed architecture-dependent, and developers should consider this when designing neural language models for real-world applications.
Recommendations
- ✓ Further research should focus on evaluating the effectiveness of CL mechanisms across multiple datasets and scenarios.
- ✓ Developers of neural language models should consider employing catastrophic forgetting mitigation strategies to prevent performance degradation over time.
