Domain Incremental Lifelong Learning in an Open World
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.
645db2f1a19f3e64bbf2ec21
Yi Dai
claimed_verified
2023-05-15T07:34:25.226Z
6319c5f31f2193ba170ae67b
/avatars/1ada6b127226a4df170b21d4f6006806.svg
daiyi
user
sdf14r1r
645db2f1a19f3e64bbf2ec22
Hao Lang
645db2f1a19f3e64bbf2ec23
Yinhe Zheng
admin_assigned
2023-06-02T13:03:45.254Z
5fa0fa89a13e063b8b2b5d1e
https://cdn-avatars.huggingface.co/v1/production/uploads/1653978482840-5fa0fa89a13e063b8b2b5d1e.jpeg
Silver
user
silver
645db2f1a19f3e64bbf2ec24
Bowen Yu
claimed_verified
2024-06-24T07:37:26.905Z
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/avatars/b5ad98cf269ae5f1fe90861fb4170fae.svg
Bowen Yu
user
Tigerph
645db2f1a19f3e64bbf2ec25
Fei Huang
2023-06-02T14:51:38.486Z
645db2f1a19f3e64bbf2ec26
Yongbin Li
Domain Incremental Lifelong Learning in an Open World
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.
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.
lifelong learning architecture-based prompt-enhanced language model task-level prompts instance-level prompts prompt key vectors unseen tasks