Neural Machine Translation for Low Resource Languages request for proposals
This Research Award is now closed
Award Recipients
Cristina España i Bonet
Deutsches Forschungszentrum für Künstliche Intelligenz
RFP: NLP - 2019
Matt Post
Johns Hopkins University
RFP: NLP - 2019
Robert West
EPFL
RFP: NLP - 2019
Applications are now closed
Application Dates
Notifications will be sent by email to selected applicants by July 28, 2019.
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Launch Date April 5, 2019
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Deadline May 31, 2019
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Winners Selected July 28, 2019
Areas of Interest
Research topics should be relevant to low resource machine translation, including, but not limited to:
- Unsupervised neural machine translation for low resource language pairs
- Semi-supervised neural machine translation for low resource language pairs
- Pretraining methods leveraging monolingual data
- Multilingual neural machine translation for low resource languages
Applicants are encouraged to demonstrate the effectiveness of the proposed method on actual low resource settings (such as Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English) as opposed to artificial settings obtained through data ablation.
Requirements
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
Eligibility
- 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 academicrelations@fb.com.
Terms & Conditions
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