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We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long. Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.
This is a multilingual parallel corpus created from translations of the Bible compiled by Christos Christodoulopoulos and Mark Steedman. 102 languages, 5,148 bitexts total number of files: 107 total number of tokens: 56.43M total number of sentence fragments: 2.84M
This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia. Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia.
This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. No claims of intellectual property are made on the work of preparation of the corpus.
This dataset is designed to provide training data for common sense relationships pulls together from various sources. The dataset is multi-lingual. See langauge codes and language info here: This dataset provides an interface for the conceptnet5 csv file, and some (but not all) of the raw text data used to build conceptnet5: omcsnet_sentences_free.txt, and omcsnet_sentences_more.txt. One use of this dataset would be to learn to extract the conceptnet relationship from the omcsnet sentences. Conceptnet5 has 34,074,917 relationships. Of those relationships, there are 2,176,099 surface text sentences related to those 2M entries. omcsnet_sentences_free has 898,161 lines. omcsnet_sentences_more has 2,001,736 lines. Original downloads are available here For more information, see: The omcsnet data comes with the following warning from the authors of the above site: Remember: this data comes from various forms of crowdsourcing. Sentences in these files are not necessarily true, useful, or appropriate.
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example: [PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] . The shared task of CoNLL-2002 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task will be offered training and test data for at least two languages. They will use the data for developing a named-entity recognition system that includes a machine learning component. Information sources other than the training data may be used in this shared task. We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training). The train/validation/test sets are available in Spanish and Dutch. For more details see and
This dataset contains 30,000 messages drawn from events including an earthquake in Haiti in 2010, an earthquake in Chile in 2010, floods in Pakistan in 2010, super-storm Sandy in the U.S.A. in 2012, and news articles spanning a large number of years and 100s of different disasters. The data has been encoded with 36 different categories related to disaster response and has been stripped of messages with sensitive information in their entirety. Upon release, this is the featured dataset of a new Udacity course on Data Science and the AI4ALL summer school and is especially utile for text analytics and natural language processing (NLP) tasks and models. The input data in this job contains thousands of untranslated disaster-related messages and their English translations.
Original source: Website and documentatuion from the European Central Bank, compiled and made available by Alberto Simoes (thank you very much!) 19 languages, 170 bitexts total number of files: 340 total number of tokens: 757.37M total number of sentence fragments: 30.55M
EiTB-ParCC: Parallel Corpus of Comparable News. A Basque-Spanish parallel corpus provided by Vicomtech (, extracted from comparable news produced by the Basque public broadcasting group Euskal Irrati Telebista.
This is a parallel corpus made out of PDF documents from the European Medicines Agency. All files are automatically converted from PDF to plain text using pdftotext with the command line arguments -layout -nopgbrk -eol unix. There are some known problems with tables and multi-column layouts - some of them are fixed in the current version. source: 22 languages, 231 bitexts total number of files: 41,957 total number of tokens: 311.65M total number of sentence fragments: 26.51M
In October 2012, the European Union's (EU) Directorate General for Education and Culture ( DG EAC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-six languages. This resource bears the name EAC Translation Memory, short EAC-TM. EAC-TM covers up to 26 languages: 22 official languages of the EU (all except Irish) plus Icelandic, Croatian, Norwegian and Turkish. EAC-TM thus contains translations from English into the following 25 languages: Bulgarian, Czech, Danish, Dutch, Estonian, German, Greek, Finnish, French, Croatian, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish and Turkish. All documents and sentences were originally written in English (source language is English) and then translated into the other languages. The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language.
In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages. This resource bears the name EAC Translation Memory, short EAC-TM. ECDC-TM covers 25 languages: the 23 official languages of the EU plus Norwegian (Norsk) and Icelandic. ECDC-TM was created by translating from English into the following 24 languages: Bulgarian, Czech, Danish, Dutch, English, Estonian, Gaelige (Irish), German, Greek, Finnish, French, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian (NOrsk), Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish and Swedish. All documents and sentences were thus originally written in English. They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg.
EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations. It consists of more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.
HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio de Sanidad, Consumo y Bienestar Social. The dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.
A parallel corpus of KDE4 localization files (v.2). 92 languages, 4,099 bitexts total number of files: 75,535 total number of tokens: 60.75M total number of sentence fragments: 8.89M
The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament notes. Each config contains the data corresponding to a different corpus. For example, "all_wiki" only includes examples from Spanish Wikipedia. By default, the config is set to "combined" which loads all the corpora; with this setting you can also specify the number of samples to return per corpus by configuring the "split" argument.
We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs sampled from the Google Natural Questions dataset, aligned across 26 typologically diverse languages (260k question-answer pairs in total). For each query we collected new passage-independent answers. These queries and answers were then human translated into 25 Non-English languages.
The Microsoft Terminology Collection can be used to develop localized versions of applications that integrate with Microsoft products. It can also be used to integrate Microsoft terminology into other terminology collections or serve as a base IT glossary for language development in the nearly 100 languages available. Terminology is provided in .tbx format, an industry standard for terminology exchange.
Parallel corpora from Web Crawls collected in the ParaCrawl project and further processed for making it a multi-parallel corpus by pivoting via English. Here we only provide the additional language pairs that came out of pivoting. The bitexts for English are available from the ParaCrawl release. 40 languages, 669 bitexts total number of files: 40 total number of tokens: 10.14G total number of sentence fragments: 505.48M Please, acknowledge the ParaCrawl project at This version is derived from the original release at their website adjusted for redistribution via the OPUS corpus collection. Please, acknowledge OPUS as well for this service.
A parallel corpus of News Commentaries provided by WMT for training SMT. The source is taken from CASMACAT: 12 languages, 63 bitexts total number of files: 61,928 total number of tokens: 49.66M total number of sentence fragments: 1.93M
This is a new collection of translated movie subtitles from IMPORTANT: If you use the OpenSubtitle corpus: Please, add a link to to your website and to your reports and publications produced with the data! This is a slightly cleaner version of the subtitle collection using improved sentence alignment and better language checking. 62 languages, 1,782 bitexts total number of files: 3,735,070 total number of tokens: 22.10G total number of sentence fragments: 3.35G
OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side. The corpus covers 100 languages (including English).OPUS-100 contains approximately 55M sentence pairs. Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 have at least 10k.
This is a collection of copyright free books aligned by Andras Farkas, which are available from Note that the texts are rather dated due to copyright issues and that some of them are manually reviewed (check the meta-data at the top of the corpus files in XML). The source is multilingually aligned, which is available from In OPUS, the alignment is formally bilingual but the multilingual alignment can be recovered from the XCES sentence alignment files. Note also that the alignment units from the original source may include multi-sentence paragraphs, which are split and sentence-aligned in OPUS. All texts are freely available for personal, educational and research use. Commercial use (e.g. reselling as parallel books) and mass redistribution without explicit permission are not granted. Please acknowledge the source when using the data! 16 languages, 64 bitexts total number of files: 158 total number of tokens: 19.50M total number of sentence fragments: 0.91M
A collection of translation memories provided by the JRC. Source: 25 languages, 299 bitexts total number of files: 817,410 total number of tokens: 2.13G total number of sentence fragments: 113.52M
A parallel corpus of GNOME localization files. Source: 187 languages, 12,822 bitexts total number of files: 113,344 total number of tokens: 267.27M total number of sentence fragments: 58.12M
Parallel corpora from Web Crawls collected in the ParaCrawl project 40 languages, 41 bitexts total number of files: 20,995 total number of tokens: 21.40G total number of sentence fragments: 1.12G
A parallel corpus of Ubuntu localization files. Source: 244 languages, 23,988 bitexts total number of files: 30,959 total number of tokens: 29.84M total number of sentence fragments: 7.73M
This is a corpus of parallel sentences extracted from Wikipedia by Krzysztof Wołk and Krzysztof Marasek. Please cite the following publication if you use the data: Krzysztof Wołk and Krzysztof Marasek: Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs., Procedia Technology, 18, Elsevier, p.126-132, 2014 20 languages, 36 bitexts total number of files: 114 total number of tokens: 610.13M total number of sentence fragments: 25.90M
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts This dataset contains the developed parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.
PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages. This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. English language is available by default. All translated pairs are sourced from examples in PAWS-Wiki. For further details, see the accompanying paper: PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification ( NOTE: There might be some missing or wrong labels in the dataset and we have replaced them with -1.
A parallel corpus originally extracted from The original documents are written in English and have been partly translated into 21 languages. The original manuals contain about 500,000 words. The amount of actually translated texts varies for different languages between 50,000 and 380,000 words. The corpus is rather noisy and may include parts from the English original in some of the translations. The corpus is tokenized and each language pair has been sentence aligned. 23 languages, 252 bitexts total number of files: 71,414 total number of tokens: 3.28M total number of sentence fragments: 1.38M
The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform. Developed by: Qatar Computing Research Institute, Arabic Language Technologies Group The QED Corpus is made public for RESEARCH purpose only. The corpus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Copyright Qatar Computing Research Institute. All rights reserved. 225 languages, 9,291 bitexts total number of files: 271,558 total number of tokens: 371.76M total number of sentence fragments: 30.93M
A parallel corpus of full-text scientific articles collected from Scielo database in the following languages: English, Portuguese and Spanish. The corpus is sentence aligned for all language pairs, as well as trilingual aligned for a small subset of sentences. Alignment was carried out using the Hunalign algorithm.
This dataset add sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them.
An unannotated Spanish corpus of nearly 1.5 billion words, compiled from different resources from the web. This resources include the spanish portions of SenSem, the Ancora Corpus, some OPUS Project Corpora and the Europarl, the Tibidabo Treebank, the IULA Spanish LSP Treebank, and dumps from the Spanish Wikipedia, Wikisource and Wikibooks. This corpus is a compilation of 100 text files. Each line of these files represents one of the 50 million sentences from the corpus.
This is a collection of Quran translations compiled by the Tanzil project The translations provided at this page are for non-commercial purposes only. If used otherwise, you need to obtain necessary permission from the translator or the publisher. If you are using more than three of the following translations in a website or application, we require you to put a link back to this page to make sure that subsequent users have access to the latest updates. 42 languages, 878 bitexts total number of files: 105 total number of tokens: 22.33M total number of sentence fragments: 1.01M
A freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences and translations for particular linguistic constructions and words. The paraphrase corpus is created by populating a graph with Tatoeba sentences and equivalence links between sentences “meaning the same thing”. This graph is then traversed to extract sets of paraphrases. Several language-independent filters and pruning steps are applied to remove uninteresting sentences. A manual evaluation performed on three languages shows that between half and three quarters of inferred paraphrases are correct and that most remaining ones are either correct but trivial, or near-paraphrases that neutralize a morphological distinction. The corpus contains a total of 1.9 million sentences, with 200 – 250 000 sentences per language. It covers a range of languages for which, to our knowledge,no other paraphrase dataset exists.
This is a collection of translated sentences from Tatoeba 359 languages, 3,403 bitexts total number of files: 750 total number of tokens: 65.54M total number of sentence fragments: 8.96M
A parallel corpus of TED talk subtitles provided by CASMACAT: The files are originally provided by 15 languages, 14 bitexts total number of files: 28 total number of tokens: 67.67M total number of sentence fragments: 3.81M
The core of WIT3 is the TED Talks corpus, that basically redistributes the original content published by the TED Conference website ( Since 2007, the TED Conference, based in California, has been posting all video recordings of its talks together with subtitles in English and their translations in more than 80 languages. Aside from its cultural and social relevance, this content, which is published under the Creative Commons BYNC-ND license, also represents a precious language resource for the machine translation research community, thanks to its size, variety of topics, and covered languages. This effort repurposes the original content in a way which is more convenient for machine translation researchers.
This is the Tilde MODEL Corpus – Multilingual Open Data for European Languages. The data has been collected from sites allowing free use and reuse of its content, as well as from Public Sector web sites. The activities have been undertaken as part of the ODINE Open Data Incubator for Europe, which aims to support the next generation of digital businesses and fast-track the development of new products and services. The corpus includes the following parts: Tilde MODEL - EESC is a multilingual corpus compiled from document texts of European Economic and Social Committee document portal. Source: Tilde MODEL - RAPID multilingual parallel corpus is compiled from all press releases of Press Release Database of European Commission released between 1975 and end of 2016 as available from Tilde MODEL - ECB multilingual parallel corpus is compiled from the multilingual pages of European Central Bank web site Tilde MODEL - EMA is a corpus compiled from texts of European Medicines Agency document portal as available in at the end of 2016 Tilde MODEL - World Bank is a corpus compiled from texts of World Bank as available in in 2017 Tilde MODEL - Travel Destinations is a multilingual parallel corpus compiled from description texts of travel destinations as available in in 2017 Tilde MODEL - is a multilingual parallel corpus compiled from Riga tourist attractions description texts of web site in 2017 Tilde MODEL - Lithuanian National Philharmonic Society is a parallel corpus compiled from texts of Lithuanian National Philharmonic Society web site in 2017 Tilde MODEL - is a parallel corpus from texts of Müpa Budapest - web site of Hungarian national culture house and concert venue compiled in spring of 2017 Tilde MODEL - is a parallel corpus from texts of portal of the best of Latvian and foreign creative industries as compiled in spring of 2017 Tilde MODEL - is a multilingual parallel corpus from texts of portal compiled in spring of 2017 30 languages, 274 bitexts total number of files: 125 total number of tokens: 1.43G total number of sentence fragments: 62.44M
The Universal Declaration of Human Rights (UDHR) is a milestone document in the history of human rights. Drafted by representatives with different legal and cultural backgrounds from all regions of the world, it set out, for the first time, fundamental human rights to be universally protected. The Declaration was adopted by the UN General Assembly in Paris on 10 December 1948 during its 183rd plenary meeting. The dataset includes translations of the document in 464 languages and dialects. © 1996 – 2009 The Office of the High Commissioner for Human Rights This plain text version prepared by the “UDHR in Unicode” project,
United nations general assembly resolutions: A six-language parallel corpus. This is a collection of translated documents from the United Nations originally compiled into a translation memory by Alexandre Rafalovitch, Robert Dale (see 6 languages, 15 bitexts total number of files: 6 total number of tokens: 18.87M total number of sentence fragments: 0.44M
This is a collection of translated documents from the United Nations. This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language
This parallel corpus consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish.
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
A dataset of atomic wikipedia edits containing insertions and deletions of a contiguous chunk of text in a sentence. This dataset contains ~43 million edits across 8 languages. An atomic edit is defined as an edit e applied to a natural language expression S as the insertion, deletion, or substitution of a sub-expression P such that both the original expression S and the resulting expression e(S) are well-formed semantic constituents (MacCartney, 2009). In this corpus, we release such atomic insertions and deletions made to sentences in wikipedia.
WikiLingua is a large-scale multilingual dataset for the evaluation of crosslingual abstractive summarization systems. The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages was done by aligning the images that are used to describe each how-to step in an article.
WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus.
The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words.
XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to cross-lingual natural language understanding and generation. The benchmark is composed of the following 11 tasks: - NER - POS Tagging (POS) - News Classification (NC) - MLQA - XNLI - PAWS-X - Query-Ad Matching (QADSM) - Web Page Ranking (WPR) - QA Matching (QAM) - Question Generation (QG) - News Title Generation (NTG) For more information, please take a look at
XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.