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

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

27 Oct 2023arXiv:2310.18205archive 2025-07-28

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.

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mbzuai-nlp/x-claim officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Cross-Lingual Transfer Fact Checking XLM-R

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X-CLAIM

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Adam Attention Attention Dropout BPE Cosine Annealing Dense Connections Discriminative Fine-Tuning Dropout GPT Layer Normalization Linear Layer Linear Warmup With Cosine Annealing Multi-Head Attention Residual Connection Softmax Weight Decay

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