Neural Machine Translation for Low Resource Languages request for proposals
This Research Award is now closed
Machine translation (MT) has made significant progress in recent years with a shift to neural models and rapid development of new architectures such as the transformer. However, current models trained on little parallel data tend to produce poor quality translations. This challenge is exacerbated in the context of social media, where we need to enable communication for languages with no corresponding parallel corpora or unofficial languages such as romanized versions.
We are pleased to invite the academic community to respond to this call for research proposals on low-resource MT. Applicants for the research awards will be expected to contribute to the field of low resource MT through innovative approaches to obtain strongly performing models under low-resource training conditions.
Applicants should submit a two-page proposal outlining their intended research and a budget overview of how funding will be used. Awards will be made in amounts up to $80,000 per proposal for projects up to one year in duration. Successful proposals will demonstrate innovative and compelling research that has the potential to significantly advance the state-of-the-art in the field. Award amounts will be determined at the sole discretion of the evaluation committee. Up to five projects will be awarded.
Cristina España i Bonet
Deutsches Forschungszentrum für Künstliche Intelligenz
RFP: NLP - 2019
Johns Hopkins University
RFP: NLP - 2019
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 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.
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).
For questions related to this RFP, please email firstname.lastname@example.org.
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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