1 code implementation • IJCNLP 2019 • Francisco Guzm{\'a}n, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, Marc{'}Aurelio Ranzato
For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available.
no code implementations • EMNLP 2018 • Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc{'}Aurelio Ranzato
Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs.
no code implementations • ACL 2021 • Ann Lee, Michael Auli, Marc{'}Aurelio Ranzato
Reranking models enable the integration of rich features to select a better output hypothesis within an n-best list or lattice.