S ub D oc T rans: Enhancing Document-level Machine Translation with Plug-and-play Multi-granularity Knowledge Augmentation (2025)

Abstract: Large language models (LLMs) have recently achieved remarkable progress in sentence-level machine translation, but scaling to document-level machine translation (DocMT) remains challenging, particularly in modeling long-range dependencies and discourse phenomena across sentences and paragraphs. Document translations generated by LLMs often suffer from poor consistency, weak coherence, and omission errors. To address these issues, we propose SubDocTrans, a novel DocMT framework that enables LLMs to produce high-quality translations through plug-and-play, multi-granularity knowledge extraction and integration. SubDocTrans first performs topic segmentation to divide a document into coherent topic sub-documents. For each sub-document, both global and local knowledge are extracted including bilingual summary, theme, proper nouns, topics, and transition hint. We then incorporate this multi-granularity knowledge into the prompting strategy, to guide LLMs in producing consistent, coherent, and accurate translations. We conduct extensive experiments across various DocMT tasks, and the results demonstrate the effectiveness of our framework, particularly in improving consistency and coherence, reducing omission errors, and mitigating hallucinations.

## introduction llms have demonstrated remarkable performance in natural language processing tasks @xcite @xcite @xcite . researchers have explored the application of llms to docmt, to address discourse issues, such as pronoun translation and terminological inconsistencies. @xcite demonstrated that large reasoning models † corresponding author (lrms) @xcite @xcite with chain-of-thought (cot) capabilities @xcite can redefine translation as a dynamic reasoning task for improving contextual coherence, cultural adaptation, and robustness through explicit reasoning. recent studies primarily focus on developing docmt agents @xcite @xcite . they can be categorized into two groups: doc2doc and doc2sent. the first group adopts doc2doc decoding by leveraging multi-agent collaboration for translating extremely long texts @xcite . however, they may result in sentence omissions @xcite and hallucinations @xcite . to mitigate sentence omissions, the second group adopts doc2sent decoding by decomposing documents into individual sentences @xcite . however, they often impair discourse phenomena, such as document coherence and pronoun translation accuracy. multi-agent interactions can also substantially increase the computational cost. to address these challenges, we propose a novel docmt framework denoted as subdoctrans, to help llms produce high-quality translations via plug-and-play multi-granularity knowledge augmentation. different from doc2doc and doc2sent, we propose a new translation strategy to segment documents into sub-documents. subdoctrans arguments translation with global and local knowledge extracted from sub-documents: bilingual summary, theme, proper nouns, topics, and transition hint. incorporating such multi-granularity knowledge into the prompting strategy can guide llms in producing consistent, coherent, and accurate translations. subdoctrans further adopts a sentence alignment strategy to avoid omission errors. our main contributions are summarized as follows: llms for docmt llms are leveraged to address discourse phenomena and coherence in docmt, and recent studies primarily focus on developing docmt agents @xcite @xcite . they can be categorized into two groups: doc2doc and doc2sent. transagent @xcite adopts doc2doc decoding by leveraging multi-agent collaboration to tackle the translation of extremely long texts. however, it may result in sentence omissions @xcite and hallucinations @xcite @xcite . to mitigate sentence omissions, incred @xcite and delta @xcite focus on doc2sent decoding by decomposing documents into individual sentences. however, they often impair discourse phenomena, such as document coherence and the accuracy of pronoun translation. moreover, multiple agent interactions can substantially increase the computational cost. knowledge augmentation for mt recent work leverages llms with external knowledge to improve translation quality, consistency, and informativeness @xcite . wang et al. (2023a) introduce a prompting-based framework that prepends multiple knowledge types including examples, templates, and terminologies, without modifying the nmt model. qian et al. (2023) utilize gpt-4 with style-aware instructions and human-written references to capture nuanced authorial intent. li et al. (2025) propose kat, which retrieves structured knowledge from wikidata and incorporates it via entity-aware prompting to enhance low-resource translation. existing work mainly focused on single-source or retrieval-based knowledge. differently, our subdoctrans leverages llms as agents to extract and integrate multigranularity knowledge into a unified prompting framework, which guides llms to produce coherent, consistent, and accurate outputs by effectively mitigating omission errors and hallucinations. 3 method we propose subdoctrans, a novel docmt framework that leverages the cot capabilities of lrms. by segmenting long documents into subdocuments, subdoctrans ensures inter-paragraph cohesion while enhancing translation through five knowledge augmentations: bilingual summary for modeling global context, theme for domain guidance, proper nouns for terminology consistency, topics for capturing local semantics, and transition hint for preserving seamless paragraph transitions. the framework of our subdoctrans is illustrated in figure 1, the algorithm of subdoctrans is detailed in algorithm appendix a.1, and the prompts used for each module are given in appendix a.2. ## experiments we conducted extensive experiments to answer the following research questions: backbone models we employ two gpt models of gpt-3.5-turbo-0125 and gpt-4o-mini as our backbone models, which are obtained from the official openai api . we set the temperature to 0.8 and the top-p sampling threshold to 0.8, while keeping other hyperparameters at their default values. we also employ two open-source lrms of backbone models. to accommodate documentlevel translation outputs, we set num_predict to -1, allowing unrestricted generation lengths. the maximum retry count r max is set to 20, and the o-sentence overlap is set to 3. baselines we compare subdoctrans against four baselines, all using the same llms and decoding settings. sentence treats each sentence independently without any document-level context. context @xcite adds three preceding source-target sentence pairs to the prompt to improved local coherence. doc2doc @xcite prompts the llm to translate 10 sentences in a single conversation turn. prompts the llm to translate ten sentences at once without intermediate alignment or guidance. delta @xcite performs sentence-by-sentence translation using multi-level memory prompts to guide consistency and fluency. we also include results from nllb-3.3b @xcite and googletrans, for further evaluation. metrics we adopt both quality and consistency metrics to evaluate the performance. for quality, we utilize two neural-based comet metrics. one is the sentence-level comet score (scomet), for which we use the model unbabel/wmt22-comet-da. another is the document-level comet score (dcomet), proposed by @xcite , using wmt21-comet-qe-mqm to compute reference-free scores. to assess terminological translation consistency, we use the consistency metrics of ltcr-1 and ltcr-1 f @xcite . ## conclusion this paper proposed subdoctrans, a novel docmt framework that leverages sub-document segmentation, five knowledge augmentations, and chainof-thought reasoning to address the challenges of document-level translation. by segmenting documents into overlapping sub-documents of similar topics, subdoctrans ensures seamless interparagraph cohesion and robust contextual modeling. the sentence alignment strategy further mitigates omission errors. experimental results show that our framework outperforms all advanced baselines, and effectively improves the accuracy of pronoun translation and mitigate hallucinations.