Robust Deep Learning for Natural Language Processing request for proposals
This Research Award is now closed
University of Massachusetts Amherst
RFP: NLP - 2019
RFP: NLP - 2019
Massachusetts Institute of Technology
RFP: NLP - 2019
Carnegie Mellon University
RFP: NLP - 2019
Applications are now closed
Notifications will be sent by email to selected applicants by July 28, 2019.
Launch Date April 5, 2019
Deadline May 31, 2019
Winners Announced July 28, 2019
Areas of Interest
Research topics should be relevant to understanding and improving the robustness of neural NLP systems (Machine Translation, Question Answering, Representation Learning), including but not limited to:
- Novel modeling approaches and learning methods to improve neural network's generalization capability towards diverse linguistic phenomena, such as lexical choice variation, colloquial language and style/genre variation, orthographical variation, code-switching, etc. Progress can be shown either on individual language (language family) or cross-lingually
- Empirical and theoretical results on robustness challenges facing state-of-the-art neural models for various NLP tasks (e.g. the GLUE benchmark), such as representation learning, classification, machine translation, question answering and natural language inference
- Efficient approaches to improve and evaluate adaptation to new domains/tasks with input and output distributions different from those seen during training, which demonstrate improvements on transfer in terms of quality and robustness over specific techniques such as domain adaptation, mixed domain training, few-shot learning, meta learning and other weak supervision from large amounts of unlabeled data
- Understanding and improving adversarial robustness. This includes defining what adversarial examples mean for different NLP tasks, methods for generating these examples, formal verification of model robustness, as well as techniques to defend against adversarial examples (both natural adversarial input and intentional adversarial attack)
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. Budgets should include travel costs to a workshop hosted at Facebook in Menlo Park, CA. Proposals will be accepted for projects up to $80k and lasting up to 12 months
- 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).
Recipients will be invited to attend a workshop at Menlo Park, CA in August 2020.
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.
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