Papers with DocMT

4 papers
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.

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