Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media

• URL: http://arxiv.org/abs/2310.18205v1
• Date: Fri, 27 Oct 2023 15:28:12 GMT
• Title: Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media
• Authors: Shubham Mittal, Megha Sundriyal, Preslav Nakov
• Abstract summary: Claim span identification (CSI) is an important step in fact-checking pipelines. Despite its importance to journalists and human fact-checkers, it remains a severely understudied problem. We create a novel dataset, X-CLAIM, consisting of 7K real-world claims collected from numerous social media platforms in five Indian languages and English.
• Score: 40.26888469822391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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