Applications closed

Computationally Efficient Natural Language Processing request for proposals


Natural language processing (NLP) has seen significant advances in recent years enabled by pre-training text representations on large amounts of unlabeled data. Unfortunately, this comes at the expense of large computational requirements, both at training and at inference time. The resulting models are orders of magnitude slower than what is typically used currently in real applications. This computational cost is the biggest barrier in the way of applying such models in practice and their wider adoption in the industry. Furthermore, with the rapid development of mobile devices and end-to-end encrypted communication, it is important to be able to run NLP models directly on those devices.

We are pleased to accept research proposals focused on making NLP models more efficient both at training and testing time. We hope this work will enable production applications of such models at scale and potentially make them directly runnable on mobile devices without losing the quality of models run on servers.

Applicants from the academic community are invited to submit a 1-2 page proposal outlining their intended research, budget, and estimated timeline.

Awards will range up to $80,000 for projects lasting up to 12 months. Successful proposals will demonstrate innovative and compelling research that has the potential to significantly advance technology. Up to ten projects will be awarded.

Award Recipients

Cornell University

Yoav Artzi

Massachusetts Institute of Technology

Song Han


Cho-Jui Hsieh

University of Amsterdam

Jaap Kamps

Applications Are Currently CLosed

Application Timeline

Notifications will be sent by email to selected applicants by July 28, 2019.

Launch Date

April 5, 2019


May 31, 2019

Winners Selected

July 28, 2019

Research Topics

Research topics should be relevant to computationally efficient NLP. Topics can include, but are not limited to:

  • Model compression techniques such as quantization, knowledge distillation, model pruning, etc.
  • More efficient, modular, sparse architectures
  • Universal representations which can be cached (i.e. that don’t need fine-tuning)
  • Non-parametric (lookup table-based) approaches, which can be more memory demanding but more computationally efficient

We’re especially interested in applications to the following:

  • Large, pre-trained representations
  • Machine translation models
  • On-device NLP


Proposals should include

  • A summary of the project (1-2 pages) explaining the area of focus, a description of techniques, any relevant prior work and a timeline with milestones and expected outcomes
  • A draft budget description (1 page) including an approximate cost of the award and explanation of how funds would be spent
  • Curriculum Vitae for all project participants
  • Organization details, i.e. tax information and administrative contact details


  • Awards must comply with applicable US and international laws, regulations and policies.
  • Applicants must be current full-time faculty at an accredited academic institution that awards research degrees to PhD students.
  • Applicants must be the Principal Investigator on any resulting award.
  • Applicants may submit one proposal per solicitation.
  • Organizations must be a nonprofit or non-governmental organization with recognized legal status in their respective country (equal to 501(c)(3) status under the United States Internal Revenue Code).

Additional Information

For questions related to this RFP, please email

Terms & Conditions

  • By submitting a proposal, you are authorizing Facebook to evaluate the proposal for a potential award, and you agree to the terms herein.
  • You agree that Facebook will not be required to treat any part of the proposal as confidential or protected by copyright.
  • You agree and acknowledge that personal data submitted with the proposal, including name, mailing address, phone number, and email address of you and other named researchers in the proposal may be collected, processed, stored and otherwise used by Facebook for the purposes of administering the website and evaluating the contents of the proposal.
  • You acknowledge that neither party is obligated to enter into any business transaction as a result of the proposal submission, Facebook is under no obligation to review or consider the proposal, and neither party acquires any intellectual property rights as a result of submitting the proposal.
  • Any feedback you provide to Facebook in the proposal regarding its products or services will not be treated as confidential or protected by copyright, and Facebook is free to use such feedback on an unrestricted basis with no compensation to you.