@inproceedings{wang-wang-2025-dke,
title = "{DKE}-Research at {S}em{E}val-2025 Task 7: A Unified Multilingual Framework for Cross-Lingual and Monolingual Retrieval with Efficient Language-specific Adaptation",
author = "Wang, Yuqi and
Wang, Kangshi",
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-1.22/",
pages = "155--159",
ISBN = "979-8-89176-273-2",
abstract = "This paper presents a unified framework for fact-checked claim retrieval, integrating contrastive learning with an in-batch multiple negative ranking loss and a conflict-aware batch sampler to enhance query-document alignment across languages. Additionally, we introduce language-specific adapters for efficient fine-tuning, enabling adaptation to previously unseen languages."
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<abstract>This paper presents a unified framework for fact-checked claim retrieval, integrating contrastive learning with an in-batch multiple negative ranking loss and a conflict-aware batch sampler to enhance query-document alignment across languages. Additionally, we introduce language-specific adapters for efficient fine-tuning, enabling adaptation to previously unseen languages.</abstract>
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%0 Conference Proceedings
%T DKE-Research at SemEval-2025 Task 7: A Unified Multilingual Framework for Cross-Lingual and Monolingual Retrieval with Efficient Language-specific Adaptation
%A Wang, Yuqi
%A Wang, Kangshi
%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 wang-wang-2025-dke
%X This paper presents a unified framework for fact-checked claim retrieval, integrating contrastive learning with an in-batch multiple negative ranking loss and a conflict-aware batch sampler to enhance query-document alignment across languages. Additionally, we introduce language-specific adapters for efficient fine-tuning, enabling adaptation to previously unseen languages.
%U https://aclanthology.org/2025.semeval-1.22/
%P 155-159
Markdown (Informal)
[DKE-Research at SemEval-2025 Task 7: A Unified Multilingual Framework for Cross-Lingual and Monolingual Retrieval with Efficient Language-specific Adaptation](https://aclanthology.org/2025.semeval-1.22/) (Wang & Wang, SemEval 2025)
ACL