# MFAQ: a Multilingual FAQ Dataset

arXiv: https://arxiv.org/abs/2109.12870 · HTML: https://mlsanity.com/paper/2109.12870/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Internet-Augmented Dialogue Generation](https://mlsanity.com/paper/2107.07566/) (2021)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages](https://mlsanity.com/paper/2006.06202/) (2020)
- [ConveRT: Efficient and Accurate Conversational Representations from Transformers](https://mlsanity.com/paper/1911.03688/) (2019)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [Domain-matched Pre-training Tasks for Dense Retrieval](https://mlsanity.com/paper/2107.13602/) (2021)
- [FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance](https://mlsanity.com/paper/1905.02851/) (2019)
- [Applying Deep Learning to Answer Selection: A Study and An Open Task](https://mlsanity.com/paper/1508.01585/) (2015)
- [SemEval-2015 Task 3: Answer Selection in Community Question Answering](https://mlsanity.com/paper/1911.11403/) (2019)
- [orgFAQ: A New Dataset and Analysis on Organizational FAQs and User Questions](https://mlsanity.com/paper/2009.01460/) (2020)
- [FAQ-based Question Answering via Knowledge Anchors](https://mlsanity.com/paper/1911.05930/) (2019)
- [ConveRT for FAQ Answering](https://mlsanity.com/paper/2108.00719/) (2021)

## Built on by (most cited first, 17 of 17)

- [See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning](https://mlsanity.com/paper/2301.05226/) (2023)
- [Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?](https://mlsanity.com/paper/2306.05871/) (2023)
- [Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings](https://mlsanity.com/paper/2402.17016/) (2024)
- [PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods](https://mlsanity.com/paper/2402.13350/) (2024)
- [F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World](https://mlsanity.com/paper/2603.19223/) (2026)
- [MAUPQA: Massive Automatically-created Polish Question Answering Dataset](https://mlsanity.com/paper/2305.05486/) (2023)
- [A Practical Examination of AI-Generated Text Detectors for Large Language Models](https://mlsanity.com/paper/2412.05139/) (2024)
- [Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text](https://mlsanity.com/paper/2309.07689/) (2023)
- [Gaperon: A Peppered English-French Generative Language Model Suite](https://mlsanity.com/paper/2510.25771/) (2025)
- [On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey](https://mlsanity.com/paper/2507.20783/) (2025)
- [Interpreting Indirect Answers to Yes-No Questions in Multiple Languages](https://mlsanity.com/paper/2310.13290/) (2023)
- [Synergistic Approach for Simultaneous Optimization of Monolingual, Cross-lingual, and Multilingual Information Retrieval](https://mlsanity.com/paper/2408.10536/) (2024)
- [New Textual Corpora for Serbian Language Modeling](https://mlsanity.com/paper/2405.09250/) (2024)
- [WarCov -- Large multilabel and multimodal dataset from social platform](https://mlsanity.com/paper/2406.10255/) (2024)
- [Nepali Passport Question Answering: A Low-Resource Dataset for Public Service Applications](https://mlsanity.com/paper/2603.13320/) (2026)
- [SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation](https://mlsanity.com/paper/2606.13647/) (2026)
- [ufakzeka-karar: An Open Turkish Typed-Decision Model with Order-Invariant Option Scoring](https://mlsanity.com/paper/2610.06744/) (2026)

## Abstract (arXiv)

In this paper, we present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages. Although this is significantly larger than existing FAQ retrieval datasets, it comes with its own challenges: duplication of content and uneven distribution of topics. We adopt a similar setup as Dense Passage Retrieval (DPR) and test various bi-encoders on this dataset. Our experiments reveal that a multilingual model based on XLM-RoBERTa achieves the best results, except for English. Lower resources languages seem to learn from one another as a multilingual model achieves a higher MRR than language-specific ones. Our qualitative analysis reveals the brittleness of the model on simple word changes. We publicly release our dataset, model and training script.