G^2C-MT frames DocMT context selection as path discovery on a discourse graph with semantic, adjacency, and keyword edges, using depth-biased random walks to sample context for LLM translation and reports outperformance on multiple models.
Document-level machine translation with large language models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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A topic-modeling framework measures document-level thematic consistency in translations by aligning key tokens across languages with a bilingual dictionary and scoring via cosine similarity, providing explainable insights beyond sentence-level metrics.
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G^2C-MT: Graph-Guided Context Selection for Document-Level Machine Translation
G^2C-MT frames DocMT context selection as path discovery on a discourse graph with semantic, adjacency, and keyword edges, using depth-biased random walks to sample context for LLM translation and reports outperformance on multiple models.
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An Explainable Approach to Document-level Translation Evaluation with Topic Modeling
A topic-modeling framework measures document-level thematic consistency in translations by aligning key tokens across languages with a bilingual dictionary and scoring via cosine similarity, providing explainable insights beyond sentence-level metrics.