Lost in Translation, Found in Spans: Identifying Claims in Multilingual
  Social Media
Shubham Mittal Megha Sundriyal Preslav Nakov
Claim span identification (CSI) is an important step in fact-checking
pipelines, aiming to identify text segments that contain a checkworthy claim or
assertion in a social media post. Despite its importance to journalists and
human fact-checkers, it remains a severely understudied problem, and the scarce
research on this topic so far has only focused on English. Here we aim to
bridge this gap by creating a novel dataset, X-CLAIM, consisting of 7K
real-world claims collected from numerous social media platforms in five Indian
languages and English. We report strong baselines with state-of-the-art
encoder-only language models (e.g., XLM-R) and we demonstrate the benefits of
training on multiple languages over alternative cross-lingual transfer methods
such as zero-shot transfer, or training on translated data, from a
high-resource language such as English. We evaluate generative large language
models from the GPT series using prompting methods on the X-CLAIM dataset and
we find that they underperform the smaller encoder-only language models for
low-resource languages.