The Universal Dependencies (UD) project seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for multiple languages. The first version of the dataset was released in 2015 and consisted of 10 treebanks over 10 languages. Version 2.7 released in 2020 consists of 183 treebanks over 104 languages. The annotation consists of UPOS (universal part-of-speech tags), XPOS (language-specific part-of-speech tags), Feats (universal morphological features), Lemmas, dependency heads and universal dependency labels.
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Common Voice is an audio dataset that consists of a unique MP3 and corresponding text file. There are 9,283 recorded hours in the dataset. The dataset also includes demographic metadata like age, sex, and accent. The dataset consists of 7,335 validated hours in 60 languages.
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OpenSubtitles is collection of multilingual parallel corpora. The dataset is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages.
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WMT 2016 is a collection of datasets used in shared tasks of the First Conference on Machine Translation. The conference builds on ten previous Workshops on statistical Machine Translation.
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A corpus of parallel text in 21 European languages from the proceedings of the European Parliament.
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This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. 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.
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WikiAnn is a dataset for cross-lingual name tagging and linking based on Wikipedia articles in 295 languages.
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OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. The dataset used for training multilingual models such as BART incorporates 138 GB of text.
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WMT 2018 is a collection of datasets used in shared tasks of the Third Conference on Machine Translation. The conference builds on a series of twelve previous annual workshops and conferences on Statistical Machine Translation.
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Multilingual Knowledge Questions and Answers (MKQA) is an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). The goal of this dataset is to provide a challenging benchmark for question answering quality across a wide set of languages. Answers are based on a language-independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering.
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News translation is a recurring WMT task. The test set is a collection of parallel corpora consisting of about 1500 English sentences translated into 5 languages (Czech, German, Finnish, Romanian, Russian, Turkish) and additional 1500 sentences from each of the 5 languages translated to English. For Romanian a third of the test set were released as a development set instead. For Turkish additional 500 sentence development set was released. The sentences were selected from dozens of news websites and translated by professional translators. The training data consists of parallel corpora to train translation models, monolingual corpora to train language models and development sets for tuning. Some training corpora were identical from WMT 2015 (Europarl, United Nations, French-English 10⁹ corpus, Common Crawl, Russian-English parallel data provided by Yandex, Wikipedia Headlines provided by CMU) and some were update (CzEng v1.6pre, News Commentary v11, monolingual news data). Additionally,
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XGLUE is an evaluation benchmark XGLUE,which is composed of 11 tasks that span 19 languages. For each task, the training data is only available in English. This means that to succeed at XGLUE, a model must have a strong zero-shot cross-lingual transfer capability to learn from the English data of a specific task and transfer what it learned to other languages. Comparing to its concurrent work XTREME, XGLUE has two characteristics: First, it includes cross-lingual NLU and cross-lingual NLG tasks at the same time; Second, besides including 5 existing cross-lingual tasks (i.e. NER, POS, MLQA, PAWS-X and XNLI), XGLUE selects 6 new tasks from Bing scenarios as well, including News Classification (NC), Query-Ad Matching (QADSM), Web Page Ranking (WPR), QA Matching (QAM), Question Generation (QG) and News Title Generation (NTG). Such diversities of languages, tasks and task origin provide a comprehensive benchmark for quantifying the quality of a pre-trained model on cross-lingual natural lan
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MaSS (Multilingual corpus of Sentence-aligned Spoken utterances) is an extension of the CMU Wilderness Multilingual Speech Dataset, a speech dataset based on recorded readings of the New Testament.
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Opusparcus is a paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The paraphrases are extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows.
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MultiEURLEX is a multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 languages, annotated with multiple labels from the EUROVOC taxonomy. The dataset covers 23 official EU languages from 7 language families.
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license: apache-2.0 tags: human-feedback size_categories: 100K<n<1M pretty_name: OpenAssistant Conversations
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News translation is a recurring WMT task. The test set is a collection of parallel corpora consisting of about 1500 English sentences translated into 5 languages (Chinese, Czech, Estonian, German, Finnish, Russian, Turkish) and additional 1500 sentences from each of the 7 languages translated to English. The sentences were selected from dozens of news websites and translated by professional translators.
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XL-BEL is a benchmark for cross-lingual biomedical entity linking (XL-BEL). The benchmark spans 10 typologically diverse languages.
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Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.
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EUR-Lex-Sum is a dataset for cross-lingual summarization. It is based on manually curated document summaries of legal acts from the European Union law platform. Documents and their respective summaries exist as crosslingual paragraph-aligned data in several of the 24 official European languages, enabling access to various cross-lingual and lower-resourced summarization setups. The dataset contains up to 1,500 document/summary pairs per language, including a subset of 375 cross-lingually aligned legal acts with texts available in all 24 languages.
Finnish News Corpus for Named Entity Recognition (Finer) is a corpus that consists of 953 articles (193,742 word tokens) with six named entity classes (organization, location, person, product, event,and date). The articles are extracted from the archives of Digitoday, a Finnish online technology news source.
Finnish Paraphrase Corpus is a fully manually annotated paraphrase corpus for Finnish containing 53,572 paraphrase pairs harvested from alternative subtitles and news headings. Out of all paraphrase pairs in the corpus 98% are manually classified to be paraphrases at least in their given context, if not in all contexts.
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GeoCoV19 is a large-scale Twitter dataset containing more than 524 million multilingual tweets. The dataset contains around 378K geotagged tweets and 5.4 million tweets with Place information. The annotations include toponyms from the user location field and tweet content and resolve them to geolocations such as country, state, or city level. In this case, 297 million tweets are annotated with geolocation using the user location field and 452 million tweets using tweet content.
AM2iCo is a wide-coverage and carefully designed cross-lingual and multilingual evaluation set. It aims to assess the ability of state-of-the-art representation models to reason over cross-lingual lexical-level concept alignment in context for 14 language pairs.
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Finnish chat conversation corpus and includes unscripted conversations on seven topics from people of different ages.
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Ice Hockey News Dataset is a corpus of Finnish ice hockey news, edited to be suitable for training of end-to-end news generation methods, as well as demonstrate generation of text, which was judged by journalists to be relatively close to a viable product.
MultiTACRED is a multilingual version of the large-scale TAC Relation Extraction Dataset. It covers 12 typologically diverse languages from 9 language families, and was created by the Speech & Language Technology group of DFKI by machine-translating the instances of the original TACRED dataset and automatically projecting their entity annotations. For details of the original TACRED's data collection and annotation process, see the Stanford paper. Translations are syntactically validated by checking the correctness of the XML tag markup. Any translations with an invalid tag structure, e.g. missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances).
This dataset contains orthographic samples of words in 19 languages (ar, br, de, en, eno, ent, eo, es, fi, fr, fro, it, ko, nl, pt, ru, sh, tr, zh). Each sample contains two text features: a Word (the textual representation of the word according to its orthography) and a Pronunciation (the highest-surface IPA pronunciation of the word as pronunced in its language).
News translation is a recurring WMT task. The test set is a collection of parallel corpora consisting of about 1500 English sentences translated into 5 languages (Czech, German, Finnish, French, Russian) and additional 1500 sentences from each of the 5 languages translated to English. The sentences are taken from newspaper articles for each language pair, except for French, where the test set was drawn from user-generated comments on the news articles (from Guardian and Le Monde). The translation was done by professional translators.