Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
CoRR(2024)
Abstract
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.
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