Papers with DocMT
GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation (2025.emnlp-industry)
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| Challenge: | Existing Document-level machine translation systems struggle to handle discourse-level phenomena such as pronoun resolution, lexical cohesion, and ellipsis. |
| Approach: | They propose a graph-based document-level machine translation framework that leverages Large Language Models to model translation flow and discourse structure. |
| Outcome: | The proposed framework outperforms commercial and closed systems in eight languages and six domains. |
SubDocTrans: Enhancing Document-level Machine Translation with Plug-and-play Multi-granularity Knowledge Augmentation (2025.findings-emnlp)
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| Challenge: | Document translations generated by large language models suffer from poor consistency, weak coherence, and omission errors. |
| Approach: | They propose a document-level machine translation framework that extracts knowledge from documents to produce high-quality translations. |
| Outcome: | The proposed framework improves consistency and coherence, reduces omission errors, and mitigates hallucinations. |
Exploring Discourse Structure in Document-level Machine Translation (2023.emnlp-main)
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| Challenge: | Existing methods for document-level machine translation (DocMT) are under-utilizing the context. |
| Approach: | They propose a paragraph-to-paragraph translation mode that utilizes discourse information . they propose 'speech-based' translation mode which utilizes contextual information based on the context . |
| Outcome: | The proposed method utilizes discourse information and performs better than previous methods. |
AdaDPI: Document-level Translation Adaptive Agent via Dynamic Parametric Internalization (2026.acl-long)
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| Challenge: | Existing solutions, such as memory-based agents, rely on explicit context concatenation, which leads to context dilution, high inference latency, and superficial knowledge integration. |
| Approach: | They propose an adaptive agentic framework that shifts the DocMT paradigm from static retrieval to dynamic parametric internalization. |
| Outcome: | Extensive experiments on the discourse-rich GuoFeng and IWSLT2017 datasets show that AdaDPI outperforms the SoTA baselines by more than 5 points on the consistency metric. |