1 code implementation • 7 Jul 2023 • Tharindu Cyril Weerasooriya, Sarah Luger, Saloni Poddar, Ashiqur R. KhudaBukhsh, Christopher M. Homan
Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media content.
no code implementations • 31 Mar 2021 • Allahsera Auguste Tapo, Michael Leventhal, Sarah Luger, Christopher M. Homan, Marcos Zampieri
Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data.
no code implementations • loresmt (AACL) 2020 • Allahsera Auguste Tapo, Bakary Coulibaly, Sébastien Diarra, Christopher Homan, Julia Kreutzer, Sarah Luger, Arthur Nagashima, Marcos Zampieri, Michael Leventhal
Low-resource languages present unique challenges to (neural) machine translation.
no code implementations • 31 Mar 2020 • Michael Leventhal, Allahsera Tapo, Sarah Luger, Marcos Zampieri, Christopher M. Homan
We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages.