Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909535313068032 |
|---|---|
| author | Azadi, Amirmohammad Zamani, Sina Rostamkhani, Mohammadmostafa Eetemadi, Sauleh |
| author_facet | Azadi, Amirmohammad Zamani, Sina Rostamkhani, Mohammadmostafa Eetemadi, Sauleh |
| contents | This paper describes our system for SemEval 2025 Task 7: Previously Fact-Checked Claim Retrieval. The task requires retrieving relevant fact-checks for a given input claim from the extensive, multilingual MultiClaim dataset, which comprises social media posts and fact-checks in several languages. To address this challenge, we first evaluated zero-shot performance using state-of-the-art English and multilingual retrieval models and then fine-tuned the most promising systems, leveraging machine translation to enhance crosslingual retrieval. Our best model achieved an accuracy of 85% on crosslingual data and 92% on monolingual data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval Azadi, Amirmohammad Zamani, Sina Rostamkhani, Mohammadmostafa Eetemadi, Sauleh Computation and Language This paper describes our system for SemEval 2025 Task 7: Previously Fact-Checked Claim Retrieval. The task requires retrieving relevant fact-checks for a given input claim from the extensive, multilingual MultiClaim dataset, which comprises social media posts and fact-checks in several languages. To address this challenge, we first evaluated zero-shot performance using state-of-the-art English and multilingual retrieval models and then fine-tuned the most promising systems, leveraging machine translation to enhance crosslingual retrieval. Our best model achieved an accuracy of 85% on crosslingual data and 92% on monolingual data. |
| title | Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.09011 |