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Continual Learning for Natural Language Generation in Task-oriented Dialog Systems

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arxiv 2010.00910 v1 pith:CUXXUWSG submitted 2020-10-02 cs.CL cs.LG

Fei Mi , Liangwei Chen , Mengjie Zhao , Minlie Huang , Boi Faltings This is my paper · ORCID
classification cs.CLcs.LG
keywords arperdomainscatastrophiccontinualcontinuallydialogforgettinggeneration
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Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains. To better fit real-life applications where new data come in a stream, we study NLG in a "continual learning" setting to expand its knowledge to new domains or functionalities incrementally. The major challenge towards this goal is catastrophic forgetting, meaning that a continually trained model tends to forget the knowledge it has learned before. To this end, we propose a method called ARPER (Adaptively Regularized Prioritized Exemplar Replay) by replaying prioritized historical exemplars, together with an adaptive regularization technique based on ElasticWeight Consolidation. Extensive experiments to continually learn new domains and intents are conducted on MultiWoZ-2.0 to benchmark ARPER with a wide range of techniques. Empirical results demonstrate that ARPER significantly outperforms other methods by effectively mitigating the detrimental catastrophic forgetting issue.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Continual Learning for Generative AI: From LLMs to MLLMs and Beyond

    cs.LG 2025-06 conditional novelty 4.0 of 10

    A survey that categorizes continual learning methods for generative models into architecture-based, regularization-based, and replay-based paradigms across four model families.

  2. Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A self-generated and committee-filtered synthetic rehearsal dataset lets a fine-tuned LLM nearly preserve its general benchmark scores while adding domain data.

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