Few-Shot Named Entity Recognition via Meta-Learning (Extended Abstract)

Jing LI, Billy CHIU, Shanshan FENG, Hao WANG

Research output: Book Chapters | Papers in Conference ProceedingsConference (Extended Abstracts)peer-review


Named entity recognition (NER) is typically framed as a sequence labeling problem where the entity classes are inherently entangled together because the entity number and classes in a sentence are not known in advance, leaving the N-way K-shot NER problem so far unexplored. In our TKDE paper, we first formally define a more suitable N-way K-shot setting for NER. Then we propose FewNER, a novel meta-learning approach for few-shot NER. FewNER separates the entire network into a task-independent part and a task-specific part. During training in FewNER, the task-independent part is meta-learned across multiple tasks and the task-specific part is learned for each individual task in a low-dimensional space. At test time, FewNER keeps the task-independent part fixed and adapts to a new task via gradient descent by updating only the task-specific part, resulting in it being less prone to overfitting and more computationally efficient. Compared with pre-trained language models (e.g., BERT and ELMo) which obtain the transferability in an implicit manner (i.e., relying on large-scale corpora), FewNER explicitly optimizes the capability of "learning to adapt quickly" through meta-learning. The results demonstrate that FewNER achieves state-of-the-art performance against nine baseline methods by significant margins on three adaptation experiments (i.e., intra-domain cross-type, cross-domain intra-type and cross-domain cross-type).
Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
Number of pages2
ISBN (Electronic)9798350322279
Publication statusPublished - 26 Jul 2023
Event2023 IEEE 39th International Conference on Data Engineering (ICDE) - Anaheim, United States
Duration: 3 Apr 20237 Apr 2023

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference2023 IEEE 39th International Conference on Data Engineering (ICDE)
Country/TerritoryUnited States
Internet address

Bibliographical note

Publisher Copyright:
© 2023 IEEE.


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