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Grant Award View - GA281495
Small Scalable Natural Language Models using Explicit Memory
GA ID:
GA281495
Agency:
Australian Research Council
Approval Date:
19-Jan-2023
Publish Date:
1-Feb-2023
Category:
Humanities, Arts and Social Sciences (HASS) Research
Grant Term:
19-Jan-2023 to 31-Dec-2025
Value (AUD):
$480,000.00
(GST inclusive where applicable)
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 22/23 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Small Scalable Natural Language Models using Explicit Memory
Purpose:
Deep neural networks have had spectacular success in natural language processing, seeing wide-spread deployment as part of automatic assistant devices in homes and cars, and across many valuable industries including finance, medicine and law. Fueling this success is the use of ever larger models, with exponentially increasing training resources, accompanying hardware and energy demands. This project aims to develop more compact models, based on the incorporation of an explicit searchable memory, which will dramatically reduce model size, hardware requirements and energy usage. This will make modern natural language processing more accessible, while also providing greater flexibility, allowing for more adaptable and portable technologies.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2023
Internal Reference ID:
DP23 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
The University of Melbourne
Recipient ABN:
84 002 705 224
Grant Recipient Location
Suburb:
UNIVERSITY OF MELBOURNE
Town/City:
UNIVERSITY OF MELBOURNE
Postcode:
3010
State/Territory:
VIC
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
VIC
Postcode:
3010
Country:
AUSTRALIA