
Die Studie untersucht die Wirksamkeit von Cross-Lingual Transfer Learning für Fact-Checking in der Türkei.


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cross-lingual-learning-vs-low-resource-fine-tuning-a-case-study-with-fact-checking-in-turkish


Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish


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提供十分かつ関連性のある証拠は、ファクトチェックシステムのパフォーマンス向上に貢献する可能性があります。


ru22fact-optimizing-evidence-for-multilingual-explainable-fact-checking-on-russia-ukraine-conflict


RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict



Optimized evidence enhances fact-checking systems by providing relevant and sufficient information, as demonstrated in the RU22Fact dataset.


optimizing-evidence-for-multilingual-explainable-fact-checking-on-russia-ukraine-conflict


Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict



The author explores the effectiveness of cross-lingual transfer learning for fact-checking in low-resource languages, focusing on Turkish, to address the scarcity of datasets and advance research in non-English languages.


Cross-Lingual-Learning-vs-Low-Resource-Fine-Tuning-in-Fact-Checking-A-Turkish-Case-Study


Cross-Lingual Learning vs. Low-Resource Fine-Tuning in Fact-Checking: A Turkish Case Study
