Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking

2026-09-11

AI Summary

本文通过引入固定范围切块（FRC）方法解决文档级机器翻译中的长度不匹配和质量下降问题，提高了7B大型语言模型的翻译性能。

Abstract

Advanced large language models (LLMs) with long context windows can substantially reduce input truncation in document-level machine translation (DocMT). However, direct Doc2Doc translation remains prone to n-gram repetition and progressive quality degradation. A common remedy is to segment the document into finer-grained chunks. Nonetheless, conventional rule-based chunking approaches fail to handle the length distribution mismatch between training and inference. To address this, we introduce Fixed-Range Chunking (FRC), utilizing dynamic programming to partition documents into chunks within a predefined length interval. By consistently applying FRC during training and inference, the input documents of any length are mapped to the same length distribution, substantially reducing train-test length mismatch. Centered on FRC, we propose a lightweight dual-boundary matching algorithm for chunk alignment, alongside four distinct training strategies. Experimental results show that FRC-based fine-tuning substantially improves 7B LLMs over direct Doc2Doc fine-tuning and outperforms existing DocMT methods on IWSLT2017. We further construct GlobVDoc, a 10-language test set independent of mainstream DocMT training sources, and show that FRC improves out-of-distribution document translation.

Problem

• document-level machine translation
• n-gram repetition
• quality degradation
• length distribution mismatch

Innovation

• Fixed-Range Chunking
• Dynamic Programming
• Length Distribution Mismatch
• Dual-Boundary Matching