Concepts de base
ACT-MNMT introduces a novel mechanism for Multilingual Neural Machine Translation, addressing off-target issues and improving translation performance.
Résumé
Large language models (LLMs) have shown remarkable capabilities in various scenarios, especially in multilingual neural machine translation.
The off-target issue in LLM-based translation models is addressed by ACT-MNMT, a novel supervised fine-tuning mechanism.
Trigger tokens are introduced to represent different task semantics and improve translation quality.
Extensive experiments on WMT test sets demonstrate the effectiveness of ACT-MNMT in reducing off-target phenomena.
Stats
LLMs have achieved promising performance in multilingual machine translation tasks.
ACT-MNMT achieves substantially improved performance across multiple translation directions.
Citations
"Large language models have demonstrated remarkable capabilities in various scenarios."
"ACT-MNMT introduces trigger tokens to represent different task semantics and improve translation quality."