Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
This addresses the problem of misinformation in low-resource languages like Turkish for researchers and practitioners, but it is incremental as it focuses on dataset creation and standard evaluation.
The authors tackled the lack of Turkish fact-checking datasets by introducing FCTR, a dataset of 3238 real-world claims with evidence from three organizations, and evaluated cross-lingual transfer learning for Turkish, showing the dataset's potential to advance research.
The rapid spread of misinformation through social media platforms has raised concerns regarding its impact on public opinion. While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language. Hence, there is a scarcity of datasets for other languages, including Turkish. To address this concern, we have introduced the FCTR dataset, consisting of 3238 real-world claims. This dataset spans multiple domains and incorporates evidence collected from three Turkish fact-checking organizations. Additionally, we aim to assess the effectiveness of cross-lingual transfer learning for low-resource languages, with a particular focus on Turkish. We demonstrate in-context learning (zero-shot and few-shot) performance of large language models in this context. The experimental results indicate that the dataset has the potential to advance research in the Turkish language.