Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking
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...
| Authors | Xiaotian Wang, Youyuan Lin, Zhan Shen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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