Omkar2118/datasets huggingface dataset:
KdConv
Full name: Knowledge-driven Conversation

KdConv is a Chinese multi-domain Knowledge-driven Conversation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer learning and domain adaptation.

Source: [KdConv](https://github.com/thu-coai/KdConv)

Introducing paper: KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation
STACKEX
Full name: STACKEX

STACKEX expands beyond the only existing genre (i.e., academic writing) in keyphrase generation tasks. 

Source: [One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases](https://arxiv.org/pdf/1810.05241v4.pdf)

Introducing paper: One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
SciREX
Full name: SciREX

SCIREX is a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level N-ary relation identification from scientific articles. The dataset is annotated by integrating automatic and human annotations, leveraging existing scientific knowledge resources.

Source: [SCIREX: A Challenge Dataset for Document-Level Information Extraction](https://arxiv.org/pdf/2005.00512v1.pdf)

Introducing paper: SciREX: A Challenge Dataset for Document-Level Information Extraction
CH-SIMS
Full name: CH-SIMS

CH-SIMS is a Chinese single- and multimodal sentiment analysis dataset which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis.

Source: [CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotations of Modality](https://www.aclweb.org/anthology/2020.acl-main.343.pdf)

Introducing paper: CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality
WCEP
Full name: Wikipedia Current Events Portal

The WCEP dataset for multi-document summarization (MDS) consists of short, human-written summaries about news events, obtained from the Wikipedia Current Events Portal (WCEP), each paired with a cluster of news articles associated with an event. These articles consist of sources cited by editors on WCEP, and are extended with articles automatically obtained from the Common Crawl News dataset. 

Source: [WCEP](https://github.com/complementizer/wcep-mds-dataset)

Introducing paper: A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
MATINF
Full name: Maternal and Infant Dataset

Maternal and Infant (MATINF) Dataset is a large-scale dataset jointly labeled for classification, question answering and summarization in the domain of maternity and baby caring in Chinese. An entry in the dataset includes four fields: question (Q), description (D), class (C) and answer (A).

Nearly two million question-answer pairs are collected with fine-grained human-labeled classes from a large Chinese maternity and baby caring QA site. Authors conduct both automatic and manual data cleansing and remove: (1) classes with insufficient samples; (2) entries in which the length of the description filed is less than the length of the question field; (3) data with any field longer than 256 characters; (4) human-spotted ill-formed data. After the data cleansing, MATINF is constructed with the remaining 1.07 million entries

Source: [MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization](https://arxiv.org/pdf/2004.12302v2.pdf)

Introducing paper: MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization
FOBIE
Full name: Focused Open Biological Information Extraction

The Focused Open Biology Information Extraction (FOBIE) dataset aims to support IE from Computer-Aided Biomimetics. The dataset contains ~1,500 sentences from scientific biological texts. These sentences are annotated with TRADE-OFFS and syntactically similar relations between unbounded arguments, as well as argument-modifiers.

The FOBIE dataset has been used to explore Semi-Open Relation Extraction (SORE). The code for this and instructions can be found inside the SORE folder Readme.md, or in the ReadTheDocs documentations.

Source: [FOBIE](https://github.com/rubenkruiper/FOBIE)

Introducing paper: In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts
CODA-19
Full name: CODA-19

CODA-19 is a human-annotated dataset that denotes the Background, Purpose, Method, Finding/Contribution, and Other for 10,966 English abstracts in the COVID-19 Open Research Dataset.

CODA-19 was created by 248 crowd workers from Amazon Mechanical Turk collectively within ten days. Each abstract was annotated by nine different workers, and the final labels were obtained by majority voting.

CODA-19's labels have an accuracy of 82% and an inter-annotator agreement (Cohen's kappa) of 0.74 when compared against expert labels on 129 abstracts.

Source: [CODA-19](https://github.com/windx0303/CODA-19)

Introducing paper: CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset
RWWD
Full name: Real World Worry Dataset

Real World Worry Dataset (RWWD) captures the emotional responses of UK residents to COVID-19 at a point in time where the impact of the COVID19 situation affected the lives of all individuals in the UK. The data were collected on the 6th and 7th of April 2020, a time at which the UK was under lockdown (news, 2020), and death tolls were increasing. On April 6, 5,373 people in the UK had died of the virus, and 51,608 tested positive. On the day before data collection, the Queen addressed the nation via a television broadcast. Furthermore, it was also announced that Prime Minister Boris Johnson was admitted to intensive care in a hospital for COVID-19 symptoms.

The RWWD is a ground truth dataset that used a direct survey method and obtained written accounts of people alongside data of their felt emotions while writing. As such, the dataset does not rely on third-person annotation but can resort to direct self-reported emotions. Two versions of RWWD are presented, each consisting of 2,500 English
texts representing the participants’ genuine emotional responses to Corona situation in the UK: the Long RWWD consists of texts that were openended in length and asked the participants to express their feelings as they wish. The Short RWWD asked the same people also to express their feelings in Tweet-sized texts. The latter was chosen
to facilitate the use of this dataset for Twitter data research.

Source: [Measuring Emotions in the COVID-19 Real World Worry Dataset](https://arxiv.org/pdf/2004.04225v2.pdf)

Introducing paper: Measuring Emotions in the COVID-19 Real World Worry Dataset
COVID-Q

COVID-Q consists of COVID-19 questions which have been annotated into a broad category (e.g. Transmission, Prevention) and a more specific class such that questions in the same class are all asking the same thing.

Source: [COVID-Q](https://github.com/JerryWei03/COVID-Q)

Introducing paper: What Are People Asking About COVID-19? A Question Classification Dataset
WT-WT
Full name: Will-They-Won't-They

Will-They-Won't-They (WT-WT) is a large dataset of English tweets targeted at stance detection for the rumor verification task. The dataset is constructed based on tweets that discuss five recent merger and acquisition (M&A) operations of US companies, mainly from the healthcare sector.

All the annotations are carried out by domain experts; therefore, the dataset constitutes a high-quality and reliable benchmark for future research in stance detection.

Source: [WT-WT](https://github.com/cambridge-wtwt/acl2020-wtwt-tweets)

Introducing paper: Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter
iSarcasm
Full name: iSarcasm

iSarcasm is a dataset of tweets, each labelled as either sarcastic or non_sarcastic. Each sarcastic tweet is further labelled for one of the following types of ironic speech:

- sarcasm: tweets that contradict the state of affairs and are critical towards an addressee;
- irony: tweets that contradict the state of affairs but are not obviously critical towards an addressee;
- satire: tweets that appear to support an addressee, but contain underlying disagreement and mocking;
- understatement: tweets that undermine the importance of the state of affairs they refer to;
- overstatement: tweets that describe the state of affairs in obviously exaggerated terms;
- rhetorical question: tweets that include a question whose invited inference (implicature) is obviously contradicting the state of affairs.

For each sarastic tweet, there's also:

- an explanation, in English sentences, as to why it is sarcastic, and
- a rephrase that conveys the same meaning non-sarcastically. Both have been provided by the author of the tweet.

iSarcasm contains 4,484 tweets, out of which 777 are labelled as sarcastic and 3,707 as non-sarcastic. You'll find two files, isarcasm_train.csv and isarcasm_test.csv, each containing 80% and 20% of the examples chosen at random, respectively. Each line in a file has the format tweet_id,sarcasm_label,sarcasm_type, where sarcasm_type are only defined for sarcastic tweets, as specified above.

Source: [iSarcasm](https://github.com/silviu-oprea/iSarcasm)

Introducing paper: iSarcasm: A Dataset of Intended Sarcasm
KLEJ

The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language understanding task.

Key benchmark features:

- It contains a diverse set of tasks from different domains and with different objectives.
- Most tasks are created from existing datasets but the authors also released the new sentiment analysis dataset from an e-commerce domain.
- It includes tasks which have relatively small datasets and require extensive external knowledge to solve them. It promotes the usage of transfer learning instead of training separate models from scratch.

The name KLEJ (English: GLUE) is an abbreviation for Kompleksowa Lista Ewaluacji Językowych (English: Comprehensive List of Language Evaluations) and refers to the [GLUE benchmark](/dataset/glue).

Source: [KLEJ](https://klejbenchmark.com/)

Introducing paper: KLEJ: Comprehensive Benchmark for Polish Language Understanding