Papers with terminology consistency

3 papers
Discourse Graph Guided Document Translation with Large Language Models (2026.eacl-long)

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Challenge: Recent agentic machine translation systems mitigate context window constraints but require substantial computational resources and are sensitive to memory retrieval strategies.
Approach: They propose a framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than sequential or exhaustive context.
Outcome: The proposed framework surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead.
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.
Towards Better Evaluation for Generated Patent Claims (2025.acl-long)

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Challenge: Existing studies highlight inconsistencies between automated evaluation metrics and human expert assessments for patent claims.
Approach: They propose a multi-dimensional evaluation method specifically designed for patent claims that incorporates features annotated by patent experts.
Outcome: The proposed method achieves highest correlation with human expert evaluations across all assessment criteria across all tested metrics.

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