1 citations · 1 across the 4 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
NSL-MT: Linguistically Informed Negative Samples for Efficient Machine Translation in Low-Resource Languages
Mamadou K. Keita, Christopher Homan, Huy Le
We introduce negative space learning machine translation (NSL-MT), a training method for underresourced languages, that augments limited parallel data with synthetically generated…
cs.LG2026
InstructLR: A Scalable Approach to Create Instruction Dataset for Under-Resourced Languages
Mamadou K. Keita, Sebastien Diarra, Christopher Homan +1
Effective text generation and chat interfaces for low-resource languages (LRLs) remain a challenge for state-of-the-art large language models (LLMs) to support. This is mainly due…