Abstract
Claim span identification (CSI) is an important step in fact-checking pipelines, aiming to identify text segments that contain a check-worthy 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.- Anthology ID:
- 2023.emnlp-main.236
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3887–3902
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.236
- DOI:
- 10.18653/v1/2023.emnlp-main.236
- Cite (ACL):
- Shubham Mittal, Megha Sundriyal, and Preslav Nakov. 2023. Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3887–3902, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media (Mittal et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.emnlp-main.236.pdf