로그인
통찰 - Fact-Checking
Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
Die Studie untersucht die Wirksamkeit von Cross-Lingual Transfer Learning für Fact-Checking in der Türkei.
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict
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。
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.
Cross-Lingual Learning vs. Low-Resource Fine-Tuning in Fact-Checking: A Turkish Case Study
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.
© 2024 by Linnk AI