2025
ACL
ACL 2025
Howard University-AI4PC at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval-Combining Zero-Shot Claim Extraction and KNN-Based Classification for Multilingual Claim Matching
Abstract
AbstractSemEval Task 7 introduced a dataset for multilingual and cross-lingual fact checking. We propose a system that leverages similarity matching, KNN, zero-shot classification and summarization to retrieve fact-checks for social media posts across multiple languages. Our approach achieves performance within the expected range, aligning with baseline results. Although competitive, the findings highlight the potential and challenges of zero-shot methods, providing a foundation for future research in cross-lingual information verification.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Keyword Pioneer
— knn classification
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Artificial Intelligence > Learning Paradigms > Few-Shot Learning
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Metric Learning
Machine Learning > Learning Types > Zero-Shot Learning
Artificial Intelligence > Learning Paradigms > Zero-Shot Learning
Keywords
fact verification
cross-lingual transfer
information retrieval
nearest neighbor
nearest neighbor classification
text matching
semantic similarity
zero-shot classification
multilingual model
fact checking
cross-lingual retrieval
similarity matching
claim retrieval
multilingual text processing
cross-lingual fact-checking
knn classification
knn-based classification
knn algorithm
multilingual matching