Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Performance comparison of dense retrieval models trained on WebFAQ versus Wikipedia-based datasets for low-resource language

Domaine:

natural language processing

Type de record:

dataset
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from FAQ-style schema.org annotations. In total, the data collection consists of 96 million natural question-answer (QA) pairs across 75 languages, including 47 million (49\%) non-English samples. WebFAQ further serves as the foundation for 20 monolingual retrieval benchmarks with a total size of 11.2 million QA pairs (5.9 million non-English). These datasets are carefully curated through refined filtering and near-duplicate detection, yielding high-quality resources for training and evaluating multil Research goal: How does the performance of dense retrieval models trained on WebFAQ compare to those trained on Wikipedia-based datasets like Natural Questions and HotpotQA when evaluated on the same low-resource language benchmarks using Recall@10 and nDCG@20 metrics? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.2/10.

Visit

doi.orgzenodo.org

Tasks

information retrievalquestion answering

Tags

performancedenseretrievalmodelstrainedWebFAQthoseWikipedia-based

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Degradation of Cross-Lingual Dense Retrieval MRR on Low-Resource WebFAQ Language Families Versus Monolingual BaselinesPretraining Dense Retrieval Models on WebFAQ for Zero-Shot Cross-Lingual Recall in Low-Resource XTREME SubsetsPerformance of Multilingual Dense Retrieval Models on WebFAQ Benchmarks with Cross-Lingual Contrastive Learningsamyakjain20/Dense-Retrieval-for-Low-Resource-LanguageImpact of Multilingual Dense Retriever Model Scaling on Low-Resource WebFAQ 2.0 PerformancePerformance Comparison of Zero-Shot Cross-Lingual Retrieval Models on Low-Resource Languages

Degradation of Cross-Lingual Dense Retrieval MRR on Low-Resource WebFAQ Language Families Versus Monolingual Baselines

We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from

Pretraining Dense Retrieval Models on WebFAQ for Zero-Shot Cross-Lingual Recall in Low-Resource XTREME Subsets

We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from

Performance of Multilingual Dense Retrieval Models on WebFAQ Benchmarks with Cross-Lingual Contrastive Learning

We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from

samyakjain20/Dense-Retrieval-for-Low-Resource-Language

Built a specialized query answering retrieval model designed for low-resource languages. Utilized da

Impact of Multilingual Dense Retriever Model Scaling on Low-Resource WebFAQ 2.0 Performance

We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from

Performance Comparison of Zero-Shot Cross-Lingual Retrieval Models on Low-Resource Languages

Transferring information retrieval (IR) models from a high-resource language (typically English) to