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.