Scaling Pretraining Data for Multilingual Encoders to Bridge the Performance Gap with Monolingual Models on Non-English WebFAQ

Abstract

Abstract: 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 Question

Does increasing the pretraining data scale for multilingual encoders close the performance gap with fine-tuned monolingual models on non-English WebFAQ subsets without task-specific training?

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