Papers by Emanuele Di Rosa

1 papers
Multi-Agent Orchestration for Terminology-Constrained Machine Translation in Industrial Localization (2026.acl-industry)

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Challenge: Accurate terminology is a non-negotiable requirement in industrial localization processes.
Approach: They propose a multi-agent LLM pipeline that orchestrates four specialized agents for terminology-constrained machine translation.
Outcome: The proposed system achieves 99.4% average accuracy while outperforming other systems on the WMT25 Terminology Translation benchmark.

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