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 (2025)

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.

## introduction with the rapid dissemination of information in the digital age, the global spread of misinformation has become a significant challenge. for instance, a recent study @xcite found that false news spreads around six times faster than true news on social media, highlighting the urgency of addressing this issue. moreover, false posts and disinformation on popular social media platforms often transcend boundaries of linguistic and cultural, reaching diverse audiences before they can be effectively countered @xcite . this dynamic underscores the critical importance of multilingual and cross-lingual natural language processing (nlp) techniques @xcite @xcite , which enable fact-checkers to break down language barriers, access and verify information across languages for the rapid identification of relevant content. therefore, such techniques are essential, as they not only enhance the efficiency and scalability of fact-checking efforts but also bridge gaps between disparate sources of information. in this paper, we propose a multilingual and cross-lingual information retrieval (clir) system designed to enhance fact-checked claim retrieval in a multilingual context. we present a unified framework for both cross-lingual and monolingual retrieval tasks, demonstrating the effectiveness of our system through detailed experiments. our method combines a contrastive learning approach with a conflict-aware batch sampler to improve the alignment of query-document pairs in different languages. additionally, we introduce languagespecific adapters for efficient fine-tuning, which significantly improves the performance of the system on unseen languages. ## background multilingual and clir have evolved significantly, transitioning from lexical matching to semanticaware neural architectures. early approaches relied on statistic-based lexical methods @xcite , which performed exact term matching but struggled with cross-lingual lexical gaps, such as polysemy or morphological variations across languages @xcite . to address these limitations, translation-based clir methods emerged, leveraging machine translation (mt) to bridge languages. these methods either translate queries into the document language (query translation) or documents into the query language (document translation) @xcite . however, such approaches were prone to error propagation from imperfect mt systems, particularly for morphologically rich or under-resourced languages @xcite . the advent of pre-trained multilingual language representation models, such as multilingual bert @xcite and xlm-r @xcite , marked a paradigm shift for dense retrieval. these models enabled embedding queries and documents in different languages into a shared semantic space. for example, the xlm-r-based models, such as multilingual e5 @xcite and bge-m3 @xcite , leverage contrastive pre-training on large multilingual corpora to align cross-lingual representations, capturing meaningful semantic relationships among multiple languages and better generalize across linguistic and cultural contexts. ## system overview our multilingual and clir system is designed to address the challenges of cross-lingual and monolingual retrieval for different languages in a unified framework. the system leverages a multilingual e5 @xcite (m-e5) model, pretrained on extensive corpora including more than 100 languages, as the backbone for the languageagnostic representations and further enhances it with language-specific adapters for parameterefficient adaptation for each language indicated in this task. the system is fine-tuned using contrastive learning with an in-batch multiple negative ranking loss, enhanced with the conflict-aware batch sampling constraint, ensuring better alignment across languages. below, we describe the key components of the system in detail. ## conclusion in this paper, we introduced a unified multilingual framework for cross-lingual and monolingual fact-checked claim retrieval, leveraging contrastive learning, conflict-aware batch sampling, and language-specific adapters. our approach effectively improves retrieval performance by aligning multilingual representations while maintaining language-specific features. the integration of lowrank adapters allows efficient adaptation to individual languages, with a strategy for handling unseen languages based on morphologically similar counterparts. experimental results on the semeval 2025 task 7 dataset demonstrate the effectiveness of our method, achieving strong performance across multiple languages. future work will explore extending our approach to more low-resource languages and further optimizing retrieval efficiency in realworld applications.