@inproceedings{nayak-etal-2025-ju,
title = "{JU}{\_}{NLP} at {S}em{E}val-2025 Task 7: Leveraging Transformer-Based Models for Multilingual {\&} Crosslingual Fact-Checked Claim Retrieval",
author = "Nayak, Atanu and
Debnath, Srijani and
Majumdar, Arpan and
Pal, Pritam and
Das, Dipankar",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.271/",
pages = "2084--2089",
ISBN = "979-8-89176-273-2",
abstract = "Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper presents a systematic approach for the retrieval of top-k relevant fact-checks for a given post in a monolingual and cross-lingual setup using transformer-based pre-trained models fine-tuned with a dual encoder architecture. By training and evaluating the shared task test dataset, our proposed best-performing framework achieved an average success@10 score of 0.79 and 0.62 for the retrieval of 10 fact-checks from the fact-check corpus against a post in monolingual and crosslingual track respectively."
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<abstract>Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper presents a systematic approach for the retrieval of top-k relevant fact-checks for a given post in a monolingual and cross-lingual setup using transformer-based pre-trained models fine-tuned with a dual encoder architecture. By training and evaluating the shared task test dataset, our proposed best-performing framework achieved an average success@10 score of 0.79 and 0.62 for the retrieval of 10 fact-checks from the fact-check corpus against a post in monolingual and crosslingual track respectively.</abstract>
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%0 Conference Proceedings
%T JU_NLP at SemEval-2025 Task 7: Leveraging Transformer-Based Models for Multilingual & Crosslingual Fact-Checked Claim Retrieval
%A Nayak, Atanu
%A Debnath, Srijani
%A Majumdar, Arpan
%A Pal, Pritam
%A Das, Dipankar
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F nayak-etal-2025-ju
%X Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper presents a systematic approach for the retrieval of top-k relevant fact-checks for a given post in a monolingual and cross-lingual setup using transformer-based pre-trained models fine-tuned with a dual encoder architecture. By training and evaluating the shared task test dataset, our proposed best-performing framework achieved an average success@10 score of 0.79 and 0.62 for the retrieval of 10 fact-checks from the fact-check corpus against a post in monolingual and crosslingual track respectively.
%U https://aclanthology.org/2025.semeval-1.271/
%P 2084-2089
Markdown (Informal)
[JU_NLP at SemEval-2025 Task 7: Leveraging Transformer-Based Models for Multilingual & Crosslingual Fact-Checked Claim Retrieval](https://aclanthology.org/2025.semeval-1.271/) (Nayak et al., SemEval 2025)
ACL