March 2, 2018

Low Resource Neural Machine Translation request for proposals

About

This Research Award is now closed

At Facebook, we care about building community across the globe. To do so, we need to break down language barriers using machine translation. One of the big challenges is to achieve great translation accuracy in the absence of large quantities of parallel corpora. This is especially true for Neural Machine Translation (NMT) which uses models with a large amount of parameters.

Facebook is pleased to invite the academic community to respond to this call for research proposals on low-resource Neural Machine Translation. Applicants for the research awards will be expected to contribute to the field of low-resource NMT through research into novel, strongly performing models under low-resource training conditions and/or comparable corpora mining techniques for low-resource language pairs.

Applicants should submit a two-page proposal outlining their intended research and a budget overview of how funding will be used. Additionally, participants need to submit a timeline with quarterly milestones.

Awards will be made in amounts ranging from $20,000 to $40,000 per proposal for projects up to one year in duration beginning June 2018. Successful proposals will demonstrate innovative and compelling research that has the potential to significantly advance technology. Award amounts will be determined at the sole discretion of the evaluation committee. Participants need to be prepared to show milestone completion and present results at the sixth-month mark (November 2018). Up to 4 projects will be awarded.

Representatives from each awarded project will be invited to a workshop with other participants in September 2018, and are expected to attend an evaluation meeting in late November 2018. Opportunities for a second round of funding will be determined at the November meeting. Travel costs to Menlo Park CA, USA should be included in the proposed budget. Award recipients will be listed on the Facebook Research website and will be encouraged to openly publish any findings from their work as well as make any code available as open source.

Research topics

Research topics should be relevant to low resource neural machine translation, including, but not limited to:

  • Unsupervised Neural Machine Translation for low resource language pairs
  • Comparable corpora mining for low resource language pairs
  • Exploiting monolingual resources for low resource language pairs

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.

Timing and dates

  • Applications are now closed.
  • Notification process: Successful awardees will be notified by email in early May, 2018.

Terms and 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.