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Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs

Paper proposing a method to integrate multilingual knowledge graphs into neural machine translation models.

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Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs

By Simone Conia, Daniel Lee, Min Li, Umar Farooq Minhas, Saloni Potdar, Yunyao Li
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The paper introduces XC-Translate, a benchmark for cross-cultural translation, and KG-MT, an end-to-end method that integrates information from multilingual knowledge graphs into machine translation models.

The authors demonstrate the effectiveness of their approach in translating texts containing entity names. Their method outperforms state-of-the-art approaches by a large margin.

Abstract

The paper introduces XC-Translate, a benchmark for cross-cultural translation, and KG-MT, an end-to-end method that integrates information from multilingual knowledge graphs into machine translation models. The authors demonstrate the effectiveness of their approach in translating texts containing entity names. Their method outperforms state-of-the-art approaches by a large margin.

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machine translationmultilingual knowledge graphscross-cultural translationneural machine translation modelsentity namesKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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