WebFAQ 2.0: A Multilingual QA Dataset with Mined Hard Negatives for Dense Retrieval

• URL: http://arxiv.org/abs/2602.17327v1
• Date: Thu, 19 Feb 2026 12:45:58 GMT
• Title: WebFAQ 2.0: A Multilingual QA Dataset with Mined Hard Negatives for Dense Retrieval
• Authors: Michael Dinzinger, Laura Caspari, Ali Salman, Irvin Topi, Jelena Mitrović, Michael Granitzer,
• Abstract summary: WebFAQ 2.0 is a new version of the WebFAQ dataset, containing 198 million FAQ-based natural question-answer pairs across 108 languages.<n>Compared to the previous version, it significantly expands multilingual coverage and the number of bilingual aligned QA pairs to over 14.3M.<n>In response to community feedback, we also release a hard negatives dataset for training dense retrievers, with 1.25M queries across 20 languages.
• Score: 2.043884585536348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce WebFAQ 2.0, a new version of the WebFAQ dataset, containing 198 million FAQ-based natural question-answer pairs across 108 languages. Compared to the previous version, it significantly expands multilingual coverage and the number of bilingual aligned QA pairs to over 14.3M, making it the largest FAQ-based resource. Unlike the original release, WebFAQ 2.0 uses a novel data collection strategy that directly crawls and extracts relevant web content, resulting in a substantially more diverse and multilingual dataset with richer context through page titles and descriptions. In response to community feedback, we also release a hard negatives dataset for training dense retrievers, with 1.25M queries across 20 languages. These hard negatives were mined using a two-stage retrieval pipeline and include cross-encoder scores for 200 negatives per query. We further show how this resource enables two primary fine-tuning strategies for dense retrievers: Contrastive Learning with MultipleNegativesRanking loss, and Knowledge Distillation with MarginMSE loss. WebFAQ 2.0 is not a static resource but part of a long-term effort. Since late 2025, structured FAQs are being regularly released through the Open Web Index, enabling continuous expansion and refinement. We publish the datasets and training scripts to facilitate further research in multilingual and cross-lingual IR. The dataset itself and all related resources are publicly available on GitHub and HuggingFace.

Related papers

• Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning [51.203811759364925]
  mKGQAgent breaks down the task of converting natural language questions into SPARQL queries into modular, interpretable subtasks.<n> Evaluated on the DBpedia- and Corporate-based KGQA benchmarks within the Text2SPARQL challenge 2025, our approach took first place among the other participants.
  arXiv  Detail & Related papers  (2025-07-22T19:23:03Z)
• IndicRAGSuite: Large-Scale Datasets and a Benchmark for Indian Language RAG Systems [17.88837706307504]
  IndicMSMarco is a multilingual benchmark for evaluating retrieval quality and response generation in 13 Indian languages.<n>We build a large-scale dataset of (question, answer, relevant passage)s derived from the Wikipedias of 19 Indian languages using state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2025-06-02T12:55:51Z)
• WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval [0.8478469524684645]
  WebFAQ is a large-scale collection of open-domain question answering datasets derived from FAQ-style schema.org annotations.<n>In total, the data collection consists of 96 million natural question-answer (QA) pairs across 75 languages, including 47 million (49%) non-English samples.<n>WebFAQ serves as the foundation for 20 monolingual retrieval benchmarks with a total size of 11.2 million QA pairs.
  arXiv  Detail & Related papers  (2025-02-28T10:46:52Z)
• XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples [64.79218405438871]
  We introduce XAMPLER: Cross-Lingual Example Retrieval, a method tailored to tackle the challenge of cross-lingual in-context learning.<n>XAMPLER first trains a retriever based on Glot500, a multilingual small language model.<n>It can directly retrieve English examples as few-shot examples for in-context learning of target languages.
  arXiv  Detail & Related papers  (2024-05-08T15:13:33Z)
• From Multiple-Choice to Extractive QA: A Case Study for English and Arabic [51.13706104333848]
  We explore the feasibility of repurposing an existing multilingual dataset for a new NLP task.<n>We present annotation guidelines and a parallel EQA dataset for English and Modern Standard Arabic.<n>We aim to help others adapt our approach for the remaining 120 BELEBELE language variants, many of which are deemed under-resourced.
  arXiv  Detail & Related papers  (2024-04-26T11:46:05Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension [61.079852289005025]
  Cross-lingual question answering over knowledge base (xKBQA) aims to answer questions in languages different from that of the provided knowledge base. One of the major challenges facing xKBQA is the high cost of data annotation. We propose a novel approach for xKBQA in a reading comprehension paradigm.
  arXiv  Detail & Related papers  (2023-02-26T05:52:52Z)
• Semantic Parsing for Conversational Question Answering over Knowledge Graphs [63.939700311269156]
  We develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task. Our dataset and models are released at https://github.com/Edinburgh/SPICE.
  arXiv  Detail & Related papers  (2023-01-28T14:45:11Z)
• MFAQ: a Multilingual FAQ Dataset [9.625301186732598]
  We present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages. We adopt a similar setup as Dense Passage Retrieval (DPR) and test various bi-encoders on this dataset.
  arXiv  Detail & Related papers  (2021-09-27T08:43:25Z)
• Multilingual Compositional Wikidata Questions [9.602430657819564]
  We propose a method for creating a multilingual, parallel dataset of question-Query pairs grounded in Wikidata. We use this data to train semantics for Hebrew, Kannada, Chinese and English to better understand the current strengths and weaknesses of multilingual semantic parsing.
  arXiv  Detail & Related papers  (2021-08-07T19:40:38Z)
• XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation [100.09099800591822]
  XGLUE is a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models. XGLUE provides 11 diversified tasks that cover both natural language understanding and generation scenarios.
  arXiv  Detail & Related papers  (2020-04-03T07:03:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.