title: Domain Incremental Lifelong Learning in an Open World
summary: Lifelong learning (LL) is an important ability for NLP models to learn new
  tasks continuously. Architecture-based approaches are reported to be effective
  implementations for LL models. However, it is non-trivial to extend previous
  approaches to domain incremental LL scenarios since they either require access
  to task identities in the testing phase or cannot handle samples from unseen
  tasks. In this paper, we propose Diana: a
  dynamic architecture-based
  lifelong learning model that tries to learn a sequence
  of tasks with a prompt-enhanced language model. Four types of hierarchically
  organized prompts are used in Diana to capture knowledge from different
  granularities. Specifically, we dedicate task-level prompts to capture
  task-specific knowledge to retain high LL performances and maintain
  instance-level prompts to learn knowledge shared across input samples to
  improve the model's generalization performance. Moreover, we dedicate separate
  prompts to explicitly model unseen tasks and introduce a set of prompt key
  vectors to facilitate knowledge sharing between tasks. Extensive experiments
  demonstrate that Diana outperforms state-of-the-art LL models, especially in
  handling unseen tasks. We release the code and data at
  https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/diana.
paper_authors._id: 645db2f1a19f3e64bbf2ec21
paper_authors.name: Yi Dai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-15T07:34:25.226Z
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paper_authors.fullname: daiyi
paper_authors.type: user
paper_authors.user: sdf14r1r
paper_authors._id: 645db2f1a19f3e64bbf2ec22
paper_authors.name: Hao Lang
paper_authors._id: 645db2f1a19f3e64bbf2ec23
paper_authors.name: Yinhe Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:03:45.254Z
paper_authors._id: 5fa0fa89a13e063b8b2b5d1e
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paper_authors.fullname: Silver
paper_authors.type: user
paper_authors.user: silver
paper_authors._id: 645db2f1a19f3e64bbf2ec24
paper_authors.name: Bowen Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-24T07:37:26.905Z
paper_authors._id: 63d9d68c1cae35c27bf7a6a7
paper_authors.avatarUrl: /avatars/b5ad98cf269ae5f1fe90861fb4170fae.svg
paper_authors.fullname: Bowen Yu
paper_authors.type: user
paper_authors.user: Tigerph
paper_authors._id: 645db2f1a19f3e64bbf2ec25
paper_authors.name: Fei Huang
paper_authors.statusLastChangedAt: 2023-06-02T14:51:38.486Z
paper_authors._id: 645db2f1a19f3e64bbf2ec26
paper_authors.name: Yongbin Li
paper_title: Domain Incremental Lifelong Learning in an Open World
paper_summary: Lifelong learning (LL) is an important ability for NLP models to learn new
  tasks continuously. Architecture-based approaches are reported to be effective
  implementations for LL models. However, it is non-trivial to extend previous
  approaches to domain incremental LL scenarios since they either require access
  to task identities in the testing phase or cannot handle samples from unseen
  tasks. In this paper, we propose Diana: a
  dynamic architecture-based
  lifelong learning model that tries to learn a sequence
  of tasks with a prompt-enhanced language model. Four types of hierarchically
  organized prompts are used in Diana to capture knowledge from different
  granularities. Specifically, we dedicate task-level prompts to capture
  task-specific knowledge to retain high LL performances and maintain
  instance-level prompts to learn knowledge shared across input samples to
  improve the model's generalization performance. Moreover, we dedicate separate
  prompts to explicitly model unseen tasks and introduce a set of prompt key
  vectors to facilitate knowledge sharing between tasks. Extensive experiments
  demonstrate that Diana outperforms state-of-the-art LL models, especially in
  handling unseen tasks. We release the code and data at
  https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/diana.
paper_ai_summary: Diana is a dynamic architecture-based lifelong learning model using prompt-enhanced language models to handle a sequence of tasks, including unseen tasks, with improved generalization and performance.
paper_ai_keywords: lifelong learning architecture-based prompt-enhanced language model task-level prompts instance-level prompts prompt key vectors unseen tasks