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Found 4 papers, 1 papers with code
Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning
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.
Domain-specific MT for Low-resource Languages: The case of Bambara-French
Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data.
Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara
Low-resource languages present unique challenges to (neural) machine translation.
Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study
We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages.
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