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LWSD 2023 : Learning with Small Data
LWSD 2023 : Learning with Small Data

LWSD 2023 : Learning with Small Data

Gothenburg, Sweden
Event Date: September 11, 2023 - September 12, 2023
Submission Deadline: May 05, 2023
Notification of Acceptance: June 12, 2023
Camera Ready Version Due: August 14, 2023




Call for Papers


Learning with Small Data
September 11-12, 2023
CLASP, University of Gothenburg

Come visit us in Gothenburg!

Great topic, free registration, proceedings published in the ACL anthology, amazing keynote speakers:

Aurélie Herbelot -- University of Trento
Tal Linzen -- NYU & Google
Danielle Matthews -- University of Sheffield

https://sites.google.com/view/learning-with-small-data/home

There is now an acute need for intensive research on the possibility of effective learning with small data. Our 2023 conference, LSD, is devoted to work on this problem, with application to computational linguistics.

Learning with Small Data will bring together researchers from various areas to discuss the sustainability of current state of the art methods in computational linguistics which rely on very large models, such as GPT2-3, BERT, and XLNet. The conference encourages contributions from machine learning, computational linguistics, theoretical linguistics, philosophy, cognitive science, and psycholinguistics, as well as from artificial intelligence ethics and social policy. We hope to see innovative technical proposals, and we will cultivate a wide spectrum of views within a lively dialog on the issues that the conference addresses.

The workshop is organized by CLASP, University of Gothenburg.

https://gu-clasp.github.io/

## Important Dates:

Submission deadline: 2023 May 5, anywhere on Earth
Notification of acceptance: 2023 June 12, anywhere on Earth
Camera ready: 2023 August 14, anywhere on Earth
Conference: 2023 September 11-12, not anywhere on Earth, but in Gothenburg

Additional details in our website:

https://sites.google.com/view/learning-with-small-data/home





Credits and Sources

[1] LWSD 2023 : Learning with Small Data


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