A hierarchical QA framework converts RST discourse trees into enhanced sentence representations for structure-guided retrieval and reports consistent gains over baselines on four datasets across genres and languages.
Capturing longer context for document-level neural machine translation: A multi-resolutional approach
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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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Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering
A hierarchical QA framework converts RST discourse trees into enhanced sentence representations for structure-guided retrieval and reports consistent gains over baselines on four datasets across genres and languages.
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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.