A novel benchmark framework for linguistic QA retrieval, empirical evidence supporting monolingual IR-specialised models, and insights into retrieval robustness under paraphrastic variation are included, enabling improved QA systems for specialised and low-resource environments.
The results show that English retrieval is already strong under BM25, whereas translated isiZulu queries benefit more clearly from multilingual semantic retrieval, especially at Top-10, and suggest that multilingual semantic retrieval can partly reduce translated-query barriers in open repository access.
Yamin Lu, Zhiqiang Huang, Tang Cheng· Information Development· 0 citations
MTEB-PT is presented, a Portuguese benchmark constructed from a subset of MMTEB, comprising 14 existing datasets across Semantic Textual Similarity (STS), classification, retrieval, and reranking, and shows that language-specific fine-tuning still improves model performance in Portuguese, especially on task types that match the adaptation data most closely.
Lucas H.T. Okamura, Alexandre Alcoforado, A. H. R. Costa· 1 citation
This paper proposes SeSyCo, a Semantic-Symbolic Knowledge Consensus framework, which leverages the semantic space to diverge monolingual queries into broad multilingual evidence, and subsequently utilize the symbolic space to eliminate language discrepancies, converging the gathered information into a robust consensus for precise SPARQL generation.
Yu Zhang, Ran Song, Xiaofei Gao et al.· Proceedings of the 32nd ACM...· 0 citations
A query-level analysis shows that BM25 excels at named-entity queries, while dense and hybrid methods improve natural-language, noisy, cross-lingual, and concept queries, and overall CUP enables real-world evaluation of Greek retrieval across lexical, semantic, noisy, and cross-lingual queries.
Katerina Papantoniou, P. Papadakos, T. Patkos et al.· 0 citations
DocuMind is a fully offline, privacy-preserving, multilingual Document Question Answering system built on the Retrieval-Augmented Generation (RAG) architecture, enabling true cross-lingual retrieval without any translation step.
S. S, Sripalreddy· International Scientific Jou...· 0 citations
Cross-lingual information retrieval (CLIR) for low-resource regional languages remains challenging due to limited annotated training data, morphological complexity, and linguistic diversity. This paper systematically investigates Indonesian–Javanese CLIR by integrating traditional sparse lexical retrieval, multilingual dense retrieval, and advanced large language model (LLM)-based reranking within a multi-stage retrieval framework. To evaluate our approach, comprehensive experiments are conducted on the Indonesian–Javanese subset of the CLIRMatrix benchmark, comprising 20,000 Javanese documents, 1,000 Indonesian queries, and 11,000 graded relevance judgments. We compare BM25 with query and document translation, multilingual bi-encoder retrieval, and various LLM-based pointwise reranking methods. The experimental results show that BM25 combined with document translation provides the strongest initial baseline, achieving a Mean Average Precision (MAP) of 0.45, Mean Reciprocal Rank (MRR) of 0.71, and nDCG@10 of 0.55. While LLM-based reranking consistently improves weaker dense retrieval baselines, it often degrades search performance when applied directly on top of strong BM25-based retrieval. To address this, Reciprocal Rank Fusion (RRF) is introduced, which mitigates this degradation issue and achieves the best overall performance, reaching a MAP of 0.47, MRR of 0.74, and nDCG@10 of 0.57. These empirical findings highlight the critical importance of robust initial retrieval baselines and demonstrate that LLM-based reranking is most effective when combined with rank fusion strategies in low-resource CLIR settings.
Raden Mohamad Adrian Ramadhan Hendar Wibawa, Ika Alfina, Evi Yulianti· International Conference on...· 0 citations
