6 citations · 11 across the 5 of their papers we have counts for
5 papers
Rater Cohesion and Quality from a Vicarious Perspective
Deepak Pandita, Tharindu Cyril Weerasooriya, Sujan Dutta +5
Human feedback is essential for building human-centered AI systems across domains where disagreement is prevalent, such as AI safety, content moderation, or sentiment analysis. Man…
Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning
Tharindu Cyril Weerasooriya, Sarah Luger, Saloni Poddar +2
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 conten…
Domain-specific MT for Low-resource Languages: The case of Bambara-French
Allahsera Auguste Tapo, Michael Leventhal, Sarah Luger +2
Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-spec…
Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara
Allahsera Auguste Tapo, Bakary Coulibaly, Sébastien Diarra +6
Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires si…
Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study
Michael Leventhal, Allahsera Tapo, Sarah Luger +2
We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages. Malian university student…