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cs.CL2026
Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent
Raphael Merx, Ekaterina Vylomova, Trevor Cohn
Translation quality depends on purpose: the same source text demands different translations depending on audience, tone, and communicative intent. Yet MT models and metrics treat t…
cs.CL2026
Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG
David Samuel Setiawan, Raphaël Merx, Jey Han Lau
Neural Machine Translation (NMT) models for low-resource languages suffer significant performance degradation under domain shift. We quantify this challenge using Dhao, an indigeno…