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

Paper recorded by Signals 4 on 2026-09-11 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.CL · 自然语言处理 · first seen 2026-09-14

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 hand

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#65 most recent of 186 cs.CL papers we have recorded · ↑ newer: Residual Vector-based Reconstruction as Long-Context Recall Regardless · ↓ older: Distance generalization in transformers: why bother with positional en
Cite this page: Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking: the #65 most recent of 186 cs.CL papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/doc2frc-length-consistent-document-level-machine-translation-via-fixed-range-chu.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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