ACT-MNMT: Auto-Constriction Turning for Multilingual Neural Machine Translation

Core Concepts

ACT-MNMT introduces a novel mechanism for Multilingual Neural Machine Translation, addressing off-target issues and improving translation performance.

Abstract

• 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.

Quotes

"Large language models have demonstrated remarkable capabilities in various scenarios."
"ACT-MNMT introduces trigger tokens to represent different task semantics and improve translation quality."

Key Insights Distilled From

ACT-MNMT Auto-Constriction Turning for Multilingual Neural Machine Translation

by Shaojie Dai,... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06745.pdf

Deeper Inquiries

어떻게 ACT-MNMT는 다국어 기계 번역에서 다른 세밀 조정 메커니즘과 비교되는가?

ACT-MNMT는 다른 세밀 조정 메커니즘과 비교했을 때 뛰어난 성능을 보입니다. 이 연구에서 제안된 ACT-MNMT는 자동 제약 템플릿을 활용하여 모델의 출력을 안내하는 데 사용됩니다. 이 방법은 다른 조정 방법보다 더 나은 번역 품질을 제공하며, 지시 사항을 이해하고 번역 문제를 완화하는 데 도움이 됩니다. 특히, ACT-MNMT는 다른 모델 크기에서도 효과적이며, 작은 데이터 크기에서도 높은 번역 품질을 보입니다.