IVUL-KAUST/MOLE huggingface dataset:
Dataset: 20min-XD
Description: A French-German, document-level comparable corpus of news articles from the Swiss online news outlet 20 Minuten/20 minutes. It contains 15,000 article pairs from 2015-2024, automatically aligned based on semantic similarity, exhibiting a broad spectrum of cross-lingual similarity.
Language: multilingual
Domain: news articles
Form: text
Size: 15000 documents
Provider:  University of Zurich, 20 Minuten (TX Group)
Tasks: machine translation, other
License: custom
Year: 2025
Authors: Michelle Wastl, Jannis Vamvas, Selena Calleri, Rico Sennrich
Affiliations: Department of Computational Linguistics, University of Zurich, 20 Minuten (TX Group)
Abstract: We present 20min-XD (20 Minuten cross-lingual document-level), a French-German, document-level comparable corpus of news articles, sourced from the Swiss online news outlet 20 Minuten/20 minutes. Our dataset comprises around 15,000 article pairs spanning 2015 to 2024, automatically aligned based on semantic similarity. We detail the data collection process and alignment methodology. Furthermore, we provide a qualitative and quantitative analysis of the corpus. The resulting dataset exhibits a broad spectrum of cross-lingual similarity, ranging from near-translations to loosely related articles, making it valuable for various NLP applications and broad linguistically motivated studies. We publicly release the dataset in document- and sentence-aligned versions and code for the described experiments.
Dataset: Alloprof
Description: A French question-answering dataset from the Alloprof educational help website. It contains 29,349 questions from K-12 students and their explanations, often including images and links to 2,596 reference pages, covering various school subjects like math, French, and science.
Language: multilingual
Domain: web pages
Form: text
Size: 29349 sentences
Provider: Alloprof, Mila
Tasks: question answering, information retrieval
License: MIT License
Year: 2023
Authors: Antoine Lefebvre-Brossard, Stephane Gazaille, Michel C. Desmarais
Affiliations: Mila-Quebec AI Institute, Polytechnique Montréal
Abstract: Teachers and students are increasingly relying on online learning resources to supplement the ones provided in school. This increase in the breadth and depth of available resources is a great thing for students, but only provided they are able to find answers to their queries. Question-answering and information retrieval systems have benefited from public datasets to train and evaluate their algorithms, but most of these datasets have been in English text written by and for adults. We introduce a new public French question-answering dataset collected from Alloprof, a Quebec-based primary and high-school help website, containing 29 349 questions and their explanations in a variety of school subjects from 10 368 students, with more than half of the explanations containing links to other questions or some of the 2 596 reference pages on the website. We also present a case study of this dataset in an information retrieval task. This dataset was collected on the Alloprof public forum, with all questions verified for their appropriateness and the explanations verified both for their appropriateness and their relevance to the question. To predict relevant documents, architectures using pre-trained BERT models were fine-tuned and evaluated. This dataset will allow researchers to develop question-answering, information retrieval and other algorithms specifically for the French speaking education context. Furthermore, the range of language proficiency, images, mathematical symbols and spelling mistakes will necessitate algorithms based on a multimodal comprehension. The case study we present as a baseline shows an approach that relies on recent techniques provides an acceptable performance level, but more work is necessary before it can reliably be used and trusted in a production setting.
Dataset: FREDSum
Description: A dataset of manually transcribed and annotated French political debates from 1974-2023. It is designed for multi-party dialogue summarization and includes abstractive/extractive summaries, topic segmentation, and abstractive communities annotations to support research in this area.
Language: fr
Domain: TV Channels, web pages
Form: text
Size: 142 documents
Provider: Linagora Labs
Tasks: summarization, speech recognition
License: CC BY-SA 4.0
Year: 2023
Authors: Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis
Affiliations: Linagora, France, École Polytechnique, Grenoble Ecole de Management
Abstract: Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved the performance of abstractive summarization systems. The majority of research has focused on written documents, however, neglecting the problem of multi-party dialogue summarization. In this paper, we present a dataset of French political debates for the purpose of enhancing resources for multi-lingual dialogue summarization. Our dataset consists of manually transcribed and annotated political debates, covering a range of topics and perspectives. We highlight the importance of high quality transcription and annotations for training accurate and effective dialogue summarization models, and emphasize the need for multilingual resources to support dialogue summarization in non-English languages. We also provide baseline experiments using state-of-the-art methods, and encourage further research in this area to advance the field of dialogue summarization. Our dataset will be made publicly available for use by the research community.
Dataset: Vibravox
Description: Vibravox is a GDPR-compliant dataset containing audio recordings of French speech using five different body-conduction audio sensors and a reference airborne microphone. It includes 45 hours of speech per sensor from 188 participants under various acoustic conditions, with linguistic and phonetic transcriptions.
Language: fr
Domain: wikipedia
Form: audio
Size: 273.72 hours
Provider: LMSSC
Tasks: speaker identification, speech recognition
License: CC BY 4.0
Year: 2024
Authors: Julien Hauret, Malo Olivier, Thomas Joubaud, Christophe Langrenne, Sarah Poire´e, Ve´ronique Zimpfer, E´ric Bavu
Affiliations: Laboratoire de Me´canique des Structures et des Syste`mes Couple´s, Conservatoire national des arts et me´tiers, HESAM Universite´, 75003 Paris, France, Department of Acoustics and Soldier Protection, French-German Research Institute of Saint-Louis (ISL)
Abstract: Vibravox is a dataset compliant with the General Data Protection Regulation (GDPR) containing audio recordings using five different body-conduction audio sensors: two in-ear microphones, two bone conduction vibration pickups, and a laryngophone. The dataset also includes audio data from an airborne microphone used as a reference. The Vibravox corpus contains 45 hours per sensor of speech samples and physiological sounds recorded by 188 participants under different acoustic conditions imposed by a high order ambisonics 3D spatializer. Annotations about the recording conditions and linguistic transcriptions are also included in the corpus. We conducted a series of experiments on various speech-related tasks, including speech recognition, speech enhancement, and speaker verification. These experiments were carried out using state-of-the-art models to evaluate and compare their performances on signals captured by the different audio sensors offered by the Vibravox dataset, with the aim of gaining a better grasp of their individual characteristics.
Dataset: MTNT
Description: A benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit and professionally sourced translations. It includes English comments translated into French and Japanese, as well as French and Japanese comments translated into English, on the order of 7k-37k sentences per language pair.
Language: multilingual
Domain: social media, commentary
Form: text
Size: 37930 sentences
Provider: Carnegie Mellon University
Tasks: machine translation
License: MIT License
Year: 2018
Authors: Paul Michel, Graham Neubig
Affiliations: Language Technologies Institute, Carnegie Mellon University
Abstract: Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and translations, and thus previous work has resorted to evaluating on synthetically created datasets. In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (www.reddit.com) and professionally sourced translations. We commissioned translations of English comments into French and Japanese, as well as French and Japanese comments into English, on the order of 7k-37k sentences per language pair. We qualitatively and quantitatively examine the types of noise included in this dataset, then demonstrate that existing MT models fail badly on a number of noise-related phenomena, even after performing adaptation on a small training set of in-domain data. This indicates that this dataset can provide an attractive testbed for methods tailored to handling noisy text in MT. The data is publicly available at www.cs.cmu.edu/~pmichel1/mtnt/.
Dataset: PIAF
Description: PIAF is a French Question Answering dataset that was collected through a participatory approach. The dataset consists of question-answer pairs extracted from Wikipedia articles.
Language: fr
Domain: wikipedia
Form: text
Size: 3835 sentences
Provider: Etalab
Tasks: question answering
License: MIT License
Year: 2020
Authors: Rachel Keraron, Guillaume Lancrenon, Mathilde Bras, Frédéric Allary, Gilles Moyse, Thomas Scialom, Edmundo-Pavel Soriano-Morales, Jacopo Staiano
Affiliations: reciTAL, Etalab, Sorbonne Universit'e
Abstract: Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.
Dataset: FrenchToxicityPrompts
Description: A dataset of 50,000 naturally occurring French prompts and their continuations, annotated with toxicity scores from a widely used toxicity classifier. It is designed to evaluate and mitigate toxicity in French language models.
Language: fr
Domain: social media, public datasets
Form: text
Size: 50000 sentences
Provider: NAVER LABS Europe
Tasks: offensive language detection
License: CC BY-SA 4.0
Year: 2024
Authors: Caroline Brun, Vassilina Nikoulina
Affiliations: NAVER LABS Europe
Abstract: Large language models (LLMs) are increasingly popular but are also prone to generating bias, toxic or harmful language, which can have detrimental effects on individuals and communities. Although most efforts is put to assess and mitigate toxicity in generated content, it is primarily concentrated on English, while it's essential to consider other languages as well. For addressing this issue, we create and release FrenchToxicityPrompts, a dataset of 50K naturally occurring French prompts and their continuations, annotated with toxicity scores from a widely used toxicity classifier. We evaluate 14 different models from four prevalent open-sourced families of LLMs against our dataset to assess their potential toxicity across various dimensions. We hope that our contribution will foster future research on toxicity detection and mitigation beyond Englis
Dataset: OBSINFOX
Description: A corpus of 100 French press documents from 17 unreliable sources. The documents were annotated by 8 human annotators using 11 labels (e.g., FakeNews, Subjective, Exaggeration) to analyze the characteristics of fake news.
Language: fr
Domain: news articles
Form: text
Size: 100 documents
Provider: Observatoire
Tasks: fake news detection, topic classification
License: CC BY-NC 4.0
Year: 2024
Authors: Benjamin Icard, François Maine, Morgane Casanova, Géraud Faye, Julien Chanson, Guillaume Gadek, Ghislain Atemezing, François Bancilhon, Paul Égré
Affiliations: Sorbonne Université, Institut Jean-Nicod, Freedom Partners, Université de Rennes, Airbus Defence and Space, Université Paris-Saclay, Mondeca, European Union Agency for Railways, Observatoire des Médias
Abstract: We present a corpus of 100 documents, OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteristic of fake news, and compare them to the predictions of automated classifiers. We present a topic and genre analysis using Gate Cloud, indicative of the prevalence of satire-like text in the corpus. We then use the subjectivity analyzer VAGO, and a neural version of it, to clarify the link between ascriptions of the label Subjective and ascriptions of the label Fake News. The annotated dataset is available online at the following url: https://github.com/obs-info/obsinfox   Keywords: Fake News, Multi-Labels, Subjectivity, Vagueness, Detail, Opinion, Exaggeration, French Press
Dataset: CFDD
Description: The Claire French Dialogue Dataset (CFDD) is a corpus containing roughly 160 million words from transcripts and stage plays in French.
Language: fr
Domain: captions, public datasets, web pages
Form: text
Size: 160000000 tokens
Provider: LINAGORA Labs
Tasks: language modeling, text generation
License: CC BY-NC-SA 4.0
Year: 2023
Authors: Julie Hunter, Jérôme Louradour, Virgile Rennard, Ismaïl Harrando, Guokan Shang, Jean-Pierre Lorré
Affiliations: LINAGORA
Abstract: We present the Claire French Dialogue Dataset (CFDD), a resource created by members of LINAGORA Labs in the context of the OpenLLM France initiative. CFDD is a corpus containing roughly 160 million words from transcripts and stage plays in French that we have assembled and publicly released in an effort to further the development of multilingual, open source language models. This paper describes the 24 individual corpora of which CFDD is composed and provides links and citations to their original sources. It also provides our proposed breakdown of the full CFDD dataset into eight categories of subcorpora and describes the process we followed to standardize the format of the final dataset. We conclude with a discussion of similar work and future directions.
Dataset: FREEMmax
Description: FREEMmax is a large corpus of Early Modern French (16th-18th centuries), with some texts extending to the 1920s. It aggregates texts from various sources, including institutional databases, research projects, and web scraping, covering diverse genres like literature, correspondence, and plays.
Language: fr
Domain: web pages, public datasets
Form: text
Size: 185643482 tokens
Provider: Inria, Sorbonne Universite, Universite de Geneve, LIGM, Universite Gustage Eiffel, CNRS
Tasks: language modeling
License: custom
Year: 2022
Authors: Simon Gabay, Pedro Ortiz Suarez, Alexandre Bartz, Alix Chague, Rachel Bawden, Philippe Gambette, Benoît Sagot
Affiliations: Inria, Sorbonne Universite, Universite de Geneve, LIGM, Universite Gustage Eiffel, CNRS
Abstract: Language models for historical states of language are becoming increasingly important to allow the optimal digitisation and analysis of old textual sources. Because these historical states are at the same time more complex to process and more scarce in the corpora available, specific efforts are necessary to train natural language processing (NLP) tools adapted to the data. In this paper, we present our efforts to develop NLP tools for Early Modern French (historical French from the 16$^\text{th}$ to the 18$^\text{th}$ centuries). We present the $\text{FreEM}_{\text{max}}$ corpus of Early Modern French and D'AlemBERT, a RoBERTa-based language model trained on $\text{FreEM}_{\text{max}}$. We evaluate the usefulness of D'AlemBERT by fine-tuning it on a part-of-speech tagging task, outperforming previous work on the test set. Importantly, we find evidence for the transfer learning capacity of the language model, since its performance on lesser-resourced time periods appears to have been boosted by the more resourced ones. We release D'AlemBERT and the open-sourced subpart of the $\text{FreEM}_{\text{max}}$ corpus.
Dataset: FQuAD2.0
Description: A French Question Answering dataset that extends FQuAD1.1 with over 17,000 adversarially created unanswerable questions. The questions are extracted from Wikipedia articles, and the total dataset comprises almost 80,000 questions. It is designed to train models to distinguish answerable from unanswerable questions.
Language: fr
Domain: wikipedia
Form: text
Size: 79768 sentences
Provider: Illuin Technology
Tasks: question answering
License: CC BY-NC-SA 3.0
Year: 2021
Authors: Quentin Heinrich, Gautier Viaud, Wacim Belblidia
Affiliations: Illuin Technology
Abstract: Question Answering, including Reading Comprehension, is one of the NLP research areas that has seen significant scientific breakthroughs over the past few years, thanks to the concomitant advances in Language Modeling. Most of these breakthroughs, however, are centered on the English language. In 2020, as a first strong initiative to bridge the gap to the French language, Illuin Technology introduced FQuAD1.1, a French Native Reading Comprehension dataset composed of 60,000+ questions and answers samples extracted from Wikipedia articles. Nonetheless, Question Answering models trained on this dataset have a major drawback: they are not able to predict when a given question has no answer in the paragraph of interest, therefore making unreliable predictions in various industrial use-cases. In the present work, we introduce FQuAD2.0, which extends FQuAD with 17,000+ unanswerable questions, annotated adversarially, in order to be similar to answerable ones. This new dataset, comprising a total of almost 80,000 questions, makes it possible to train French Question Answering models with the ability of distinguishing unanswerable questions from answerable ones. We benchmark several models with this dataset: our best model, a fine-tuned CamemBERT-large, achieves a F1 score of 82.3% on this classification task, and a F1 score of 83% on the Reading Comprehension task.
Dataset: XNLI
Description: Evaluation set for NLI by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages
Language: English, French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili, Urdu
Domain: public datasets
Form: text
Size: 112500 sentences
Subsets: Name: en
Volume: 7500
Unit: sentences
Language: English, Name: fr
Volume: 7500
Unit: sentences
Language: French, Name: es
Volume: 7500
Unit: sentences
Language: Spanish, Name: de
Volume: 7500
Unit: sentences
Language: German, Name: el
Volume: 7500
Unit: sentences
Language: Greek, Name: bg
Volume: 7500
Unit: sentences
Language: Bulgarian, Name: ru
Volume: 7500
Unit: sentences
Language: Russian, Name: tr
Volume: 7500
Unit: sentences
Language: Turkish, Name: ar
Volume: 7500
Unit: sentences
Language: Arabic, Name: vi
Volume: 7500
Unit: sentences
Language: Vietnamese, Name: th
Volume: 7500
Unit: sentences
Language: Thai, Name: zh
Volume: 7500
Unit: sentences
Language: Chinese, Name: hi
Volume: 7500
Unit: sentences
Language: Hindi, Name: sw
Volume: 7500
Unit: sentences
Language: Swahili, Name: ur
Volume: 7500
Unit: sentences
Language: Urdu
Provider: Facebook
Tasks: natural language inference
License: CC BY-NC 4.0
Year: 2018
Authors: Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk, Veselin Stoyanov
Affiliations: Facebook AI Research, New York University
Abstract: State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.
Dataset: X-stance
Description: A large-scale, multilingual (German, French, Italian) dataset for stance detection. It contains over 67,000 comments from Swiss political candidates on more than 150 political issues, formatted as question-comment pairs. The dataset is designed for cross-lingual and cross-target evaluation.
Language: German, French, Italian
Domain: commentary
Form: text
Size: 55502 sentences
Subsets: Name: DE
Volume: 40200
Unit: sentences
Language: German, Name: FR
Volume: 14129
Unit: sentences
Language: French, Name: IT
Volume: 1172.7
Unit: sentences
Language: Italian
Provider: Univeristy of Zurich
Tasks: stance detection
License: CC BY-NC 4.0
Year: 2020
Authors: Jannis Vamvas, Rico Sennrich
Affiliations: University of Zurich, University of Edinburgh
Abstract: We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues. To make learning across targets possible, we prepend to each instance a natural question that represents the target (e.g. "Do you support X?"). Baseline results from multilingual BERT show that zero-shot cross-lingual and cross-target transfer of stance detection is moderately successful with this approach.