Impact of Non-English Fine-Tuning on Zero-Shot Cross-Lingual Transfer in Dense Retrieval Models

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 fine-tuning dense retrieval models on WebFAQ's 47 million non-English pairs impact zero-shot cross-lingual transfer accuracy on the TyDi QA benchmark compared to English-only training?

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