Datasets:

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euronews

The corpora comprise of files per data provider that are encoded in the IOB format (Ramshaw & Marcus, 1995). The IOB format is a simple text chunking format that divides texts into single tokens per line, and, separated by a whitespace, tags to mark named entities. The most commonly used categories for tags are PER (person), LOC (location) and ORG (organization). To mark named entities that span multiple tokens, the tags have a prefix of either B- (beginning of named entity) or I- (inside of named entity). O (outside of named entity) tags are used to mark tokens that are not a named entity.

europa_eac_tm

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.

europa_ecdc_tm

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.

europarl_bilingual

A parallel corpus extracted from the European Parliament web site by Philipp Koehn (University of Edinburgh). The main intended use is to aid statistical machine translation research.

event2Mind

In Event2Mind, we explore the task of understanding stereotypical intents and reactions to events. Through crowdsourcing, we create a large corpus with 25,000 events and free-form descriptions of their intents and reactions, both of the event's subject and (potentially implied) other participants.

evidence_infer_treatment

Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text. The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of these articles will have multiple questions, or 'prompts' associated with them. These prompts will ask about the relationship between an intervention and comparator with respect to an outcome, as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared to placebo on the duration of headaches. For the sake of this task, we assume that a particular article will report that the intervention of interest either significantly increased, significantly decreased or had significant effect on the outcome, relative to the comparator. The dataset could be used for automatic data extraction of the results of a given RCT. This would enable readers to discover the effectiveness of different treatments without needing to read the paper.

exams

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.

factckbr

A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification. The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time. The FACTCK.BR dataset contains 1309 claims with its corresponding label.

fake_news_english

Fake news has become a major societal issue and a technical challenge for social media companies to identify. This content is difficult to identify because the term "fake news" covers intentionally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addressing the problem requires clear definitions and examples. In this work, we present a dataset of fake news and satire stories that are hand coded, verified, and, in the case of fake news, include rebutting stories. We also include a thematic content analysis of the articles, identifying major themes that include hyperbolic support or condemnation of a gure, conspiracy theories, racist themes, and discrediting of reliable sources. In addition to releasing this dataset for research use, we analyze it and show results based on language that are promising for classification purposes. Overall, our contribution of a dataset and initial analysis are designed to support future work by fake news researchers.

fake_news_filipino

Low-Resource Fake News Detection Corpora in Filipino. The first of its kind. Contains 3,206 expertly-labeled news samples, half of which are real and half of which are fake.

farsi_news

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

fashion_mnist

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

fever

few_rel

FewRel is a large-scale few-shot relation extraction dataset, which contains more than one hundred relations and tens of thousands of annotated instances cross different domains.

financial_phrasebank

The key arguments for the low utilization of statistical techniques in financial sentiment analysis have been the difficulty of implementation for practical applications and the lack of high quality training data for building such models. Especially in the case of finance and economic texts, annotated collections are a scarce resource and many are reserved for proprietary use only. To resolve the missing training data problem, we present a collection of ∼ 5000 sentences to establish human-annotated standards for benchmarking alternative modeling techniques. The objective of the phrase level annotation task was to classify each example sentence into a positive, negative or neutral category by considering only the information explicitly available in the given sentence. Since the study is focused only on financial and economic domains, the annotators were asked to consider the sentences from the view point of an investor only; i.e. whether the news may have positive, negative or neutral influence on the stock price. As a result, sentences which have a sentiment that is not relevant from an economic or financial perspective are considered neutral. This release of the financial phrase bank covers a collection of 4840 sentences. The selected collection of phrases was annotated by 16 people with adequate background knowledge on financial markets. Three of the annotators were researchers and the remaining 13 annotators were master’s students at Aalto University School of Business with majors primarily in finance, accounting, and economics. Given the large number of overlapping annotations (5 to 8 annotations per sentence), there are several ways to define a majority vote based gold standard. To provide an objective comparison, we have formed 4 alternative reference datasets based on the strength of majority agreement: all annotators agree, >=75% of annotators agree, >=66% of annotators agree and >=50% of annotators agree.

finer

The directory data contains a corpus of Finnish technology related news articles with a manually prepared named entity annotation (digitoday.2014.csv). The text material was extracted from the archives of Digitoday, a Finnish online technology news source (www.digitoday.fi). The corpus consists of 953 articles (193,742 word tokens) with six named entity classes (organization, location, person, product, event, and date). The corpus is available for research purposes and can be readily used for development of NER systems for Finnish.

flores

Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English.

flue

FLUE is an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language.

food101

fquad

FQuAD: French Question Answering Dataset We introduce FQuAD, a native French Question Answering Dataset. FQuAD contains 25,000+ question and answer pairs. Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.

freebase_qa

FreebaseQA is for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase The data set is generated by matching trivia-type question-answer pairs with subject-predicateobject triples in Freebase.

gap

GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia and released by Google AI Language for the evaluation of coreference resolution in practical applications.

gem

GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation, both through human annotations and automated Metrics. GEM aims to: - measure NLG progress across 13 datasets spanning many NLG tasks and languages. - provide an in-depth analysis of data and models presented via data statements and challenge sets. - develop standards for evaluation of generated text using both automated and human metrics. It is our goal to regularly update GEM and to encourage toward more inclusive practices in dataset development by extending existing data or developing datasets for additional languages.

generated_reviews_enth

`generated_reviews_enth` Generated product reviews dataset for machine translation quality prediction, part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) `generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task. This dataset (referred to as `generated_reviews_yn` in [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)) are English product reviews generated by [CTRL](https://arxiv.org/abs/1909.05858), translated by Google Translate API and annotated as accepted or rejected (`correct`) based on fluency and adequacy of the translation by human annotators. This allows it to be used for English-to-Thai translation quality esitmation (binary label), machine translation, and sentiment analysis.

generics_kb

The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.

german_legal_entity_recognition

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germaner

GermaNER is a freely available statistical German Named Entity Tagger based on conditional random fields(CRF). The tagger is trained and evaluated on the NoSta-D Named Entity dataset, which was used in the GermEval 2014 for named entity recognition. The tagger comes close to the performance of the best (proprietary) system in the competition with 77% F-measure (this is the latest result; the one reported in the paper is 76%) test set performance on the four standard NER classes (PERson, LOCation, ORGanisation and OTHer). We describe a range of features and their influence on German NER classification and provide a comparative evaluation and some analysis of the results. The software components, the training data and all data used for feature generation are distributed under permissive licenses, thus this tagger can be used in academic and commercial settings without restrictions or fees. The tagger is available as a command-line tool and as an Apache UIMA component.

germeval_14

The GermEval 2014 NER Shared Task builds on a new dataset with German Named Entity annotation with the following properties: - The data was sampled from German Wikipedia and News Corpora as a collection of citations. - The dataset covers over 31,000 sentences corresponding to over 590,000 tokens. - The NER annotation uses the NoSta-D guidelines, which extend the Tübingen Treebank guidelines, using four main NER categories with sub-structure, and annotating embeddings among NEs such as [ORG FC Kickers [LOC Darmstadt]].

giga_fren

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

gigaword

Headline-generation on a corpus of article pairs from Gigaword consisting of around 4 million articles. Use the 'org_data' provided by https://github.com/microsoft/unilm/ which is identical to https://github.com/harvardnlp/sent-summary but with better format. There are two features: - document: article. - summary: headline.

glucose

When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context.

glue

GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.

gnad10

This dataset is intended to advance topic classification for German texts. A classifier that is efffective in English may not be effective in German dataset because it has a higher inflection and longer compound words. The 10kGNAD dataset contains 10273 German news articles from an Austrian online newspaper categorized into 9 categories. Article titles and text are concatenated together and authors are removed to avoid a keyword-like classification on authors that write frequently about one category. This dataset can be used as a benchmark for German topic classification.

go_emotions

The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral. The emotion categories are admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise.

gooaq

GooAQ is a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ questions are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ answers are mined from Google's responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections.

google_wellformed_query

Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.

grail_qa

Strongly Generalizable Question Answering (GrailQA) is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). It can be used to test three levels of generalization in KBQA: i.i.d., compositional, and zero-shot.

great_code

The dataset for the variable-misuse task, described in the ICLR 2020 paper 'Global Relational Models of Source Code' [https://openreview.net/forum?id=B1lnbRNtwr] This is the public version of the dataset used in that paper. The original, used to produce the graphs in the paper, could not be open-sourced due to licensing issues. See the public associated code repository [https://github.com/VHellendoorn/ICLR20-Great] for results produced from this dataset. This dataset was generated synthetically from the corpus of Python code in the ETH Py150 Open dataset [https://github.com/google-research-datasets/eth_py150_open].

greek_legal_code

Greek_Legal_Code contains 47k classified legal resources from Greek Legislation. Its origin is “Permanent Greek Legislation Code - Raptarchis”, a collection of Greek legislative documents classified into multi-level (from broader to more specialized) categories.

guardian_authorship

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ). 2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W). 3- The same-topic/genre scenario is created by grouping all the datasts as follows. For ex., to use same_topic and split the data 60-40 use: train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[:60%]+validation[:60%]+test[:60%]') tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[-40%:]+validation[-40%:]+test[-40%:]') IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced * See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

gutenberg_time

A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg.

hans

The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn.

hansards

This release contains 1.3 million pairs of aligned text chunks (sentences or smaller fragments) from the official records (Hansards) of the 36th Canadian Parliament. The complete Hansards of the debates in the House and Senate of the 36th Canadian Parliament, as far as available, were aligned. The corpus was then split into 5 sets of sentence pairs: training (80% of the sentence pairs), two sets of sentence pairs for testing (5% each), and two sets of sentence pairs for final evaluation (5% each). The current release consists of the training and testing sets. The evaluation sets are reserved for future MT evaluation purposes and currently not available. Caveats 1. This release contains only sentence pairs. Even though the order of the sentences is the same as in the original, there may be gaps resulting from many-to-one, many-to-many, or one-to-many alignments that were filtered out. Therefore, this release may not be suitable for discourse-related research. 2. Neither the sentence splitting nor the alignments are perfect. In particular, watch out for pairs that differ considerably in length. You may want to filter these out before you do any statistical training. The alignment of the Hansards was performed as part of the ReWrite project under funding from the DARPA TIDES program.

hard

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.

harem

The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts, from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM documents are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set, a version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event, Abstraction, and Other) and a "selective" version with only 5 classes (Person, Organization, Location, Value, and Date). It's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely "Category" and "Sub-type". The dataset version processed here ONLY USE the "Category" level of the original dataset. [1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese." Brazilian Conference on Intelligent Systems. Springer, Cham, 2020.

has_part

This dataset is a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms (approximated as within a 10 year old’s vocabulary), as well as having several times more hasPart entries than in the popular ontologies ConceptNet and WordNet. In addition, it contains information about quantifiers, argument modifiers, and links the entities to appropriate concepts in Wikipedia and WordNet.

hate_offensive

hate_speech18

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.

hate_speech_filipino

Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections.

hate_speech_offensive

An annotated dataset for hate speech and offensive language detection on tweets.

hate_speech_pl

HateSpeech corpus in the current version contains over 2000 posts crawled from public Polish web. They represent various types and degrees of offensive language, expressed toward minorities (eg. ethnical, racial). The data were annotated manually.

hate_speech_portuguese

Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate').

hatexplain

Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based.

hausa_voa_ner

The Hausa VOA NER dataset is a labeled dataset for named entity recognition in Hausa. The texts were obtained from Hausa Voice of America News articles https://www.voahausa.com/ . We concentrate on four types of named entities: persons [PER], locations [LOC], organizations [ORG], and dates & time [DATE]. The Hausa VOA NER data files contain 2 columns separated by a tab ('\t'). Each word has been put on a separate line and there is an empty line after each sentences i.e the CoNLL format. The first item on each line is a word, the second is the named entity tag. The named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. For every multi-word expression like 'New York', the first word gets a tag B-TYPE and the subsequent words have tags I-TYPE, a word with tag O is not part of a phrase. The dataset is in the BIO tagging scheme. For more details, see https://www.aclweb.org/anthology/2020.emnlp-main.204/

hausa_voa_topics

A collection of news article headlines in Hausa from VOA Hausa. Each headline is labeled with one of the following classes: Nigeria, Africa, World, Health or Politics. The dataset was presented in the paper: Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages (EMNLP 2020).

hda_nli_hindi

This dataset is a recasted version of the Hindi Discourse Analysis Dataset used to train models for Natural Language Inference Tasks in Low-Resource Languages like Hindi.

head_qa

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.

health_fact

PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label. The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise. It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels. NOTE: There are missing labels in the dataset and we have replaced them with -1.

hebrew_projectbenyehuda

This repository contains a dump of thousands of public domain works in Hebrew, from Project Ben-Yehuda, in plaintext UTF-8 files, with and without diacritics (nikkud). The metadata (pseudocatalogue.csv) file is a list of titles, authors, genres, and file paths, to help you process the dump. All these works are in the public domain, so you are free to make any use of them, and do not need to ask for permission. There are 10078 files, 3181136 lines

hebrew_sentiment

HebrewSentiment is a data set consists of 12,804 user comments to posts on the official Facebook page of Israel’s president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, the first three months of Rivlin’s presidency.2 While the president’s posts aimed at reconciling tensions and called for tolerance and empathy, the sentiment expressed in the comments to the president’s posts was polarized between citizens who warmly thanked the president, and citizens that fiercely critiqued his policy. Of the 12,804 comments, 370 are neutral; 8,512 are positive, 3,922 negative. Data Annotation: A trained researcher examined each comment and determined its sentiment value, where comments with an overall positive sentiment were assigned the value 1, comments with an overall negative sentiment were assigned the value -1, and comments that are off-topic to the post’s content were assigned the value 0. We validated the coding scheme by asking a second trained researcher to code the same data. There was substantial agreement between raters (N of agreements: 10623, N of disagreements: 2105, Coehn’s Kappa = 0.697, p = 0).

hebrew_this_world

HebrewThisWorld is a data set consists of 2028 issues of the newspaper 'This World' edited by Uri Avnery and were published between 1950 and 1989. Released under the AGPLv3 license.

hellaswag

HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019.

hendrycks_test

This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more.

hind_encorp

HindEnCorp parallel texts (sentence-aligned) come from the following sources: Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008). Commentaries by Daniel Pipes contain 322 articles in English written by a journalist Daniel Pipes and translated into Hindi. EMILLE. This corpus (Baker et al., 2002) consists of three components: monolingual, parallel and annotated corpora. There are fourteen monolingual sub- corpora, including both written and (for some lan- guages) spoken data for fourteen South Asian lan- guages. The EMILLE monolingual corpora contain in total 92,799,000 words (including 2,627,000 words of transcribed spoken data for Bengali, Gujarati, Hindi, Punjabi and Urdu). The parallel corpus consists of 200,000 words of text in English and its accompanying translations into Hindi and other languages. Smaller datasets as collected by Bojar et al. (2010) include the corpus used at ACL 2005 (a subcorpus of EMILLE), a corpus of named entities from Wikipedia (crawled in 2009), and Agriculture domain parallel corpus.  For the current release, we are extending the parallel corpus using these sources: Intercorp (Čermák and Rosen,2012) is a large multilingual parallel corpus of 32 languages including Hindi. The central language used for alignment is Czech. Intercorp’s core texts amount to 202 million words. These core texts are most suitable for us because their sentence alignment is manually checked and therefore very reliable. They cover predominately short sto- ries and novels. There are seven Hindi texts in Inter- corp. Unfortunately, only for three of them the English translation is available; the other four are aligned only with Czech texts. The Hindi subcorpus of Intercorp contains 118,000 words in Hindi. TED talks 3 held in various languages, primarily English, are equipped with transcripts and these are translated into 102 languages. There are 179 talks for which Hindi translation is available. The Indic multi-parallel corpus (Birch et al., 2011; Post et al., 2012) is a corpus of texts from Wikipedia translated from the respective Indian language into English by non-expert translators hired over Mechanical Turk. The quality is thus somewhat mixed in many respects starting from typesetting and punctuation over capi- talization, spelling, word choice to sentence structure. A little bit of control could be in principle obtained from the fact that every input sentence was translated 4 times. We used the 2012 release of the corpus. Launchpad.net is a software collaboration platform that hosts many open-source projects and facilitates also collaborative localization of the tools. We downloaded all revisions of all the hosted projects and extracted the localization (.po) files. Other smaller datasets. This time, we added Wikipedia entities as crawled in 2013 (including any morphological variants of the named entitity that appears on the Hindi variant of the Wikipedia page) and words, word examples and quotes from the Shabdkosh online dictionary.

hindi_discourse

The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences. It contains sentences from stories written by 11 famous authors from the 20th Century. 4-5 stories by each author have been selected which were available in the public domain resulting in a collection of 53 stories. Most of these short stories were originally written in Hindi but some of them were written in other Indian languages and later translated to Hindi.

hippocorpus

To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide these summaries to other workers who write imagined stories. Finally, months later, we collect a retold version of the recalled stories from a subset of recalled authors. Our dataset comes paired with author demographics (age, gender, race), their openness to experience, as well as some variables regarding the author's relationship to the event (e.g., how personal the event is, how often they tell its story, etc.).

hkcancor

The Hong Kong Cantonese Corpus (HKCanCor) comprise transcribed conversations recorded between March 1997 and August 1998. It contains recordings of spontaneous speech (51 texts) and radio programmes (42 texts), which involve 2 to 4 speakers, with 1 text of monologue. In total, the corpus contains around 230,000 Chinese words. The text is word-segmented, annotated with part-of-speech (POS) tags and romanised Cantonese pronunciation. Romanisation scheme - Linguistic Society of Hong Kong (LSHK) POS scheme - Peita-Fujitsu-Renmin Ribao (PRF) corpus (Duan et al., 2000), with extended tags for Cantonese-specific phenomena added by Luke and Wang (see original paper for details).

hlgd

HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating whether the two headlines describe the same underlying world event or not.

hope_edi

A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not.

hotpot_qa

HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason with strong supervisionand explain the predictions; (4) we offer a new type of factoid comparison questions to testQA systems’ ability to extract relevant facts and perform necessary comparison.

hover

HoVer is an open-domain, many-hop fact extraction and claim verification dataset built upon the Wikipedia corpus. The original 2-hop claims are adapted from question-answer pairs from HotpotQA. It is collected by a team of NLP researchers at UNC Chapel Hill and Verisk Analytics.

hrenwac_para

The hrenWaC corpus version 2.0 consists of parallel Croatian-English texts crawled from the .hr top-level domain for Croatia. The corpus was built with Spidextor (https://github.com/abumatran/spidextor), a tool that glues together the output of SpiderLing used for crawling and Bitextor used for bitext extraction. The accuracy of the extracted bitext on the segment level is around 80% and on the word level around 84%.

hrwac

The Croatian web corpus hrWaC was built by crawling the .hr top-level domain in 2011 and again in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Croatian vs. Serbian). Version 2.0 of this corpus is described in http://www.aclweb.org/anthology/W14-0405. Version 2.1 contains newer and better linguistic annotations.

humicroedit

This new dataset is designed to assess the funniness of edited news headlines.

hybrid_qa

Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable.

hyperpartisan_news_detection

Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4. Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person. There are 2 parts: - byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed. - bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.

iapp_wiki_qa_squad

`iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles. It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset) to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in 5761/742/739 questions from 1529/191/192 articles.

id_clickbait

The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo, Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii) 15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline. Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait.

id_liputan6

In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL, an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive summarization models.

id_nergrit_corpus

Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment Analysis. id_nergrit_corpus is the Named Entity Recognition of this dataset collection which contains 18 entities as follow: 'CRD': Cardinal 'DAT': Date 'EVT': Event 'FAC': Facility 'GPE': Geopolitical Entity 'LAW': Law Entity (such as Undang-Undang) 'LOC': Location 'MON': Money 'NOR': Political Organization 'ORD': Ordinal 'ORG': Organization 'PER': Person 'PRC': Percent 'PRD': Product 'QTY': Quantity 'REG': Religion 'TIM': Time 'WOA': Work of Art 'LAN': Language

id_newspapers_2018

The dataset contains around 500K articles (136M of words) from 7 Indonesian newspapers: Detik, Kompas, Tempo, CNN Indonesia, Sindo, Republika and Poskota. The articles are dated between 1st January 2018 and 20th August 2018 (with few exceptions dated earlier). The size of uncompressed 500K json files (newspapers-json.tgz) is around 2.2GB, and the cleaned uncompressed in a big text file (newspapers.txt.gz) is about 1GB. The original source in Google Drive contains also a dataset in html format which include raw data (pictures, css, javascript, ...) from the online news website

id_panl_bppt

Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international, Science and Technology and Sport).

id_puisi

Puisi (poem) is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi with its title and author.

igbo_english_machine_translation

Parallel Igbo-English Dataset

igbo_monolingual

A dataset is a collection of Monolingual Igbo sentences.

igbo_ner

Igbo Named Entity Recognition Dataset

ilist

This dataset is introduced in a task which aimed at identifying 5 closely-related languages of Indo-Aryan language family – Hindi (also known as Khari Boli), Braj Bhasha, Awadhi, Bhojpuri, and Magahi.

imdb

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.\

imdb_urdu_reviews

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.

imppres

Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.

indic_glue

IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.

indonli

IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian. IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.

indonlp/indonlu

The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia.

inquisitive_qg

A dataset of about 20k questions that are elicited from readers as they naturally read through a document sentence by sentence. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. Because these questions are generated while the readers are processing the information, the questions directly communicate gaps between the reader’s and writer’s knowledge about the events described in the text, and are not necessarily answered in the document itself. This type of question reflects a real-world scenario: if one has questions during reading, some of them are answered by the text later on, the rest are not, but any of them would help further the reader’s understanding at the particular point when they asked it. This resource could enable question generation models to simulate human-like curiosity and cognitive processing, which may open up a new realm of applications.

interpress_news_category_tr

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.

interpress_news_category_tr_lite

It is a Turkish news data set consisting of 273601 news in 10 categories, compiled from print media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. It has been rearranged as easily separable and with fewer classes.

irc_disentangle

Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. This new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. The dataset is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context.

isixhosa_ner_corpus

Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags.

isizulu_ner_corpus

Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags.

iwslt2017

The IWSLT 2017 Multilingual Task addresses text translation, including zero-shot translation, with a single MT system across all directions including English, German, Dutch, Italian and Romanian. As unofficial task, conventional bilingual text translation is offered between English and Arabic, French, Japanese, Chinese, German and Korean.