Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent

arXiv:2606.03259 · cs.CL · Submitted 2026-06-02 · Read on arXiv

cs.CL

Submitted: 2026-06-02

Updated: 2026-08-31

Terminology

• Adaptation — 11× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• LLM judge — 5× in this paper · explained in 2 episodes
• Low-Resource Languages — 5× in this paper · explained in 4 episodes
• Core — 3× in this paper · explained in 2 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Commentary — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Error Span Annotation — 2× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Annotator Agreement — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Cross-lingual Transfer — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Pragmatics — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Gemma 3 Technical Report
• LoRA: Low-Rank Adaptation of Large Language Models
• New Trends for Modern Machine Translation with Large Reasoning Models
• No Language Left Behind: Scaling Human-Centered Machine Translation
• Fine-Grained and Multi-Dimensional Metrics for Document-Level Machine Translation
• Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation

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