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Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review). We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your model using the online evaluation tool available on klejbenchmark.com.
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 amazon.de 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.
Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles. It contains over a billion and a half words in total, out of which, there are about three million unique words. The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256. Also it was marked with two mark-up languages, namely: SGML, and XML.
A parallel news corpus in Turkish, Kurdish and English. Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper. 3 languages, 3 bitexts total number of files: 6 total number of tokens: 2.25M total number of sentence fragments: 0.14M
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 was curated from the Bing search logs (desktop users only) over the period of Jan 1st, 2020 – (Current Month - 1). Only searches that were issued many times by multiple users were included. The dataset includes queries from all over the world that had an intent related to the Coronavirus or Covid-19. In some cases this intent is explicit in the query itself (e.g., “Coronavirus updates Seattle”), in other cases it is implicit , e.g. “Shelter in place”. The implicit intent of search queries (e.g., “Toilet paper”) was extracted using random walks on the click graph as outlined in this paper by Microsoft Research. All personal data were removed.
In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional text classification baselines for judgment prediction and experimental results show that it is still a challenge for current models to predict the judgment results of legal cases, especially on prison terms. To help the researchers make improvements on legal judgment prediction.
A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil. The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were collected and aligned using the Hunalign algorithm.
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.
ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English. ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation. ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning.
The Dutch Book Review Dataset (DBRD) contains over 110k book reviews of which 22k have associated binary sentiment polarity labels. It is intended as a benchmark for sentiment classification in Dutch and created due to a lack of annotated datasets in Dutch that are suitable for this task.
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 (https://www.vicomtech.org), 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: http://www.emea.europa.eu/ 22 languages, 231 bitexts total number of files: 41,957 total number of tokens: 311.65M total number of sentence fragments: 26.51M
WebNLG is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the development and evaluation of popular tasks in the NLG pipeline architecture (Reiter and Dale, 2000), such as Discourse Ordering, Lexicalization, Aggregation and Referring Expression Generation.
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.
Contains Farsi (Persian) datasets for Machine Learning tasks, particularly NLP. These datasets have been extracted from the RSS feed of two Farsi news agency websites: - Hamshahri - RadioFarda
Giga-word corpus for French-English from WMT2010 collected by Chris Callison-Burch 2 languages, total number of files: 452 total number of tokens: 1.43G total number of sentence fragments: 47.55M
This dataset contains 93700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset.
These files contain text extracted from Stormfront, a white supremacist forum. A random set of forums posts have been sampled from several subforums and split into sentences. Those sentences have been manually labelled as containing hate speech or not, according to certain annotation guidelines.
Large Movie translated Urdu Reviews Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 40,000 highly polar movie reviews for training, and 10,000 for testing. To increase the availability of sentiment analysis dataset for a low recourse language like Urdu, we opted to use the already available IMDB Dataset. we have translated this dataset using google translator. This is a binary classification dataset having two classes as positive and negative. The reason behind using this dataset is high polarity for each class. It contains 50k samples equally divided in two classes.
It is a Turkish news data set consisting of 273601 news in 17 categories, compiled from print media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company.
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
Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text. The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations.
KILT tasks training and evaluation data. - [FEVER](https://fever.ai) | Fact Checking | fever - [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) | Entity Linking | aidayago2 - [WNED-WIKI](https://github.com/U-Alberta/wned) | Entity Linking | wned - [WNED-CWEB](https://github.com/U-Alberta/wned) | Entity Linking | cweb - [T-REx](https://hadyelsahar.github.io/t-rex) | Slot Filling | trex - [Zero-Shot RE](http://nlp.cs.washington.edu/zeroshot) | Slot Filling | structured_zeroshot - [Natural Questions](https://ai.google.com/research/NaturalQuestions) | Open Domain QA | nq - [HotpotQA](https://hotpotqa.github.io) | Open Domain QA | hotpotqa - [TriviaQA](http://nlp.cs.washington.edu/triviaqa) | Open Domain QA | triviaqa - [ELI5](https://facebookresearch.github.io/ELI5/explore.html) | Open Domain QA | eli5 - [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia) | Dialogue | wow To finish linking TriviaQA questions to the IDs provided, follow the instructions [here](http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md).
This dataset contains over 63,000 book reviews in Arabic.It is the largest sentiment analysis dataset for Arabic to-date.The book reviews were harvested from the website Goodreads during the month or March 2013.Each book review comes with the goodreads review id, the user id, the book id, the rating (1 to 5) and the text of the review.
This is LiveQA, a Chinese dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu website.
Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites. Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers. We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.
MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain). The development and test sets are available upon request.
The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge. The dataset is a collection of 18 existing QA dataset (carefully selected subset of them) and converted to the same format (SQuAD format). Among these 18 datasets, six datasets were made available for training, six datasets were made available for development, and the final six for testing. The dataset is released as part of the MRQA 2019 Shared Task.
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 http://paracrawl.eu. 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.
MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, including BioASQ, RelationExtraction, TextbookQA, contain only the test data
Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.
A parallel corpus of News Commentaries provided by WMT for training SMT. The source is taken from CASMACAT: http://www.casmacat.eu/corpus/news-commentary.html 12 languages, 63 bitexts total number of files: 61,928 total number of tokens: 49.66M total number of sentence fragments: 1.93M
Texts from the Ofis Publik ar Brezhoneg (Breton Language Board) provided by Francis Tyers 2 languages, total number of files: 278 total number of tokens: 2.12M total number of sentence fragments: 0.13M
This dataset is a compilation of the OneStopEnglish corpus of texts written at three reading levels into one file. Text documents are classified into three reading levels - ele, int, adv (Elementary, Intermediate and Advance). This dataset demonstrates its usefulness for through two applica-tions - automatic readability assessment and automatic text simplification. The corpus consists of 189 texts, each in three versions/reading levels (567 in total).
This is a new collection of translated movie subtitles from http://www.opensubtitles.org/. IMPORTANT: If you use the OpenSubtitle corpus: Please, add a link to http://www.opensubtitles.org/ 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
This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php 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 http://www.farkastranslations.com/bilingual_books.php. 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: https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory 25 languages, 299 bitexts total number of files: 817,410 total number of tokens: 2.13G total number of sentence fragments: 113.52M
The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files.
A parallel corpus of GNOME localization files. Source: https://l10n.gnome.org 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
This is a Croatian-English parallel corpus of transcribed and translated TED talks, originally extracted from https://wit3.fbk.eu. The corpus is compiled by Željko Agić and is taken from http://lt.ffzg.hr/zagic provided under the CC-BY-NC-SA license. 2 languages, total number of files: 2 total number of tokens: 2.81M total number of sentence fragments: 0.17M
A parallel corpus of Ubuntu localization files. Source: https://translations.launchpad.net 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
The OrangeSum dataset was inspired by the XSum dataset. It was created by scraping the "Orange Actu" website: https://actu.orange.fr/. Orange S.A. is a large French multinational telecommunications corporation, with 266M customers worldwide. Scraped pages cover almost a decade from Feb 2011 to Sep 2020. They belong to five main categories: France, world, politics, automotive, and society. The society category is itself divided into 8 subcategories: health, environment, people, culture, media, high-tech, unsual ("insolite" in French), and miscellaneous. Each article featured a single-sentence title as well as a very brief abstract, both professionally written by the author of the article. These two fields were extracted from each page, thus creating two summarization tasks: OrangeSum Title and OrangeSum Abstract.
A parallel corpus originally extracted from http://se.php.net/download-docs.php. 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
A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification. It is imperative to consider that the newlines were replaced with the `[n]` symbol. Please interpret them into normal newlines (for ex. `t.replace("[n]", "\n")`) and then use them for your purposes.
In Task 6-1, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and related phenomena. In Task 6-2, the participants shall distinguish between three classes of tweets: 0 (non-harmful), 1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech, some of them even putting those two phenomena in the same group. The specific conditions on which we based our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research will be summarized in an introductory paper for the task, however, the main and definitive condition to 1 distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying), or a public person/entity/large group (hate-speech).
`prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com The prachathai-67k dataset was scraped from the news site Prachathai. We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by @lukkiddd and cleaned by @cstorm125. You can also see preliminary exploration at https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb
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
scb-mt-en-th-2020: A Large English-Thai Parallel Corpus The primary objective of our work is to build a large-scale English-Thai dataset for machine translation. We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources, namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents. Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner. We train machine translation models based on this dataset. Our models' performance are comparable to that of Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is included in the training data for both Thai-English and English-Thai translation. The dataset, pre-trained models, and source code to reproduce our work are available for public use.
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.
SETimes – A Parallel Corpus of English and South-East European Languages The corpus is based on the content published on the SETimes.com news portal. The news portal publishes “news and views from Southeast Europe” in ten languages: Bulgarian, Bosnian, Greek, English, Croatian, Macedonian, Romanian, Albanian and Serbian. This version of the corpus tries to solve the issues present in an older version of the corpus (published inside OPUS, described in the LREC 2010 paper by Francis M. Tyers and Murat Serdar Alperen). The following procedures were applied to resolve existing issues: - stricter extraction process – no HTML residues present - language identification on every non-English document – non-English online documents contain English material in case the article was not translated into that language - resolving encoding issues in Croatian and Serbian – diacritics were partially lost due to encoding errors – text was rediacritized.
The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research. It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam.
This is a collection of parallel corpora collected by Hercules Dalianis and his research group for bilingual dictionary construction. More information in: Hercules Dalianis, Hao-chun Xing, Xin Zhang: Creating a Reusable English-Chinese Parallel Corpus for Bilingual Dictionary Construction, In Proceedings of LREC2010 (source: http://people.dsv.su.se/~hercules/SEC/) and Konstantinos Charitakis (2007): Using Parallel Corpora to Create a Greek-English Dictionary with UPLUG, In Proceedings of NODALIDA 2007. Afrikaans-English: Aldin Draghoender and Mattias Kanhov: Creating a reusable English – Afrikaans parallel corpora for bilingual dictionary construction 4 languages, 3 bitexts total number of files: 6 total number of tokens: 1.32M total number of sentence fragments: 0.15M
The Switchboard Dialog Act Corpus (SwDA) extends the Switchboard-1 Telephone Speech Corpus, Release 2 with turn/utterance-level dialog-act tags. The tags summarize syntactic, semantic, and pragmatic information about the associated turn. The SwDA project was undertaken at UC Boulder in the late 1990s. The SwDA is not inherently linked to the Penn Treebank 3 parses of Switchboard, and it is far from straightforward to align the two resources. In addition, the SwDA is not distributed with the Switchboard's tables of metadata about the conversations and their participants.
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
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: http://www.casmacat.eu/corpus/ted2013.html. The files are originally provided by https://wit3.fbk.eu. 15 languages, 14 bitexts total number of files: 28 total number of tokens: 67.67M total number of sentence fragments: 3.81M
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: http://dm.eesc.europa.eu/ 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 http://europa.eu/rapid/ Tilde MODEL - ECB multilingual parallel corpus is compiled from the multilingual pages of European Central Bank web site http://ebc.europa.eu/ Tilde MODEL - EMA is a corpus compiled from texts of European Medicines Agency document portal as available in http://www.ema.europa.eu/ at the end of 2016 Tilde MODEL - World Bank is a corpus compiled from texts of World Bank as available in http://www.worldbank.org/ in 2017 Tilde MODEL - AirBaltic.com Travel Destinations is a multilingual parallel corpus compiled from description texts of AirBaltic.com travel destinations as available in https://www.airbaltic.com/en/destinations/ in 2017 Tilde MODEL - LiveRiga.com is a multilingual parallel corpus compiled from Riga tourist attractions description texts of http://liveriga.com/ web site in 2017 Tilde MODEL - Lithuanian National Philharmonic Society is a parallel corpus compiled from texts of Lithuanian National Philharmonic Society web site http://www.filharmonija.lt/ in 2017 Tilde MODEL - mupa.hu is a parallel corpus from texts of Müpa Budapest - web site of Hungarian national culture house and concert venue https://www.mupa.hu/en/ compiled in spring of 2017 Tilde MODEL - fold.lv is a parallel corpus from texts of fold.lv portal http://www.fold.lv/en/ of the best of Latvian and foreign creative industries as compiled in spring of 2017 Tilde MODEL - czechtourism.com is a multilingual parallel corpus from texts of http://czechtourism.com/ 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 data set is taken from kemik group http://www.kemik.yildiz.edu.tr/ The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth. We named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study http://journals.sagepub.com/doi/abs/10.1177/0165551515620551
The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.
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 http://uncorpora.org). 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.
The WebNLG challenge consists in mapping data to text. The training data consists of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b). a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot) b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation (how to chunk the input data into sentences), lexicalisation (of the DBpedia properties), aggregation (how to avoid repetitions) and surface realisation (how to build a syntactically correct and natural sounding text).
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.
This dataset gathers 728,321 biographies from wikipedia. It aims at evaluating text generation algorithms. For each article, we provide the first paragraph and the infobox (both tokenized). For each article, we extracted the first paragraph (text), the infobox (structured data). Each infobox is encoded as a list of (field name, field value) pairs. We used Stanford CoreNLP (http://stanfordnlp.github.io/CoreNLP/) to preprocess the data, i.e. we broke the text into sentences and tokenized both the text and the field values. The dataset was randomly split in three subsets train (80%), valid (10%), test (10%).
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 https://microsoft.github.io/XGLUE/.
Yahoo Non-Factoid Question Dataset is derived from Yahoo's Webscope L6 collection using machine learning techiques such that the questions would contain non-factoid answers.The dataset contains 87,361 questions and their corresponding answers. Each question contains its best answer along with additional other answers submitted by users. Only the best answer was reviewed in determining the quality of the question-answer pair.
Yahoo! Answers Topic Classification is text classification dataset. The dataset is the Yahoo! Answers corpus as of 10/25/2007. The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. From all the answers and other meta-information, this dataset only used the best answer content and the main category information.