Omkar2118/datasets huggingface dataset:
Microsoft Research Multimodal Aligned Recipe Corpus
Full name: Microsoft Research Multimodal Aligned Recipe Corpus

To construct the MICROSOFT RESEARCH MULTIMODAL ALIGNED RECIPE CORPUS the authors first extract a large number of text and video recipes from the web. The goal is to find joint alignments between multiple text recipes and multiple video recipes for the same dish. The task is challenging, as different recipes vary in their order of instructions and use of ingredients. Moreover, video instructions can be noisy, and text and video instructions include different levels of specificity in their descriptions.

Source: [A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks](https://arxiv.org/pdf/2005.09606v1.pdf)

Introducing paper: A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks
ClarQ
Full name: ClarQ

ClarQ, consists of ∼2M examples distributed across 173 domains of stackexchange. This dataset is meant for training and evaluation of Clarification Question Generation Systems.


Source: [ClarQ: A large-scale and diverse dataset for Clarification Question Generation](https://arxiv.org/pdf/2006.05986v2.pdf)

Introducing paper: ClarQ: A large-scale and diverse dataset for Clarification Question Generation
TechQA
Full name: The TechQA Dataset

TECHQA is a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size – 600 training, 310 dev, and 490 evaluation question/answer pairs – thus reflecting the cost of creating large labeled datasets with actual data. Consequently, TECHQA is meant to stimulate research in domain adaptation rather than being a resource to build QA systems from scratch. The dataset was obtained by crawling the IBM Developer and IBM DeveloperWorks forums for questions with accepted answers that appear in a published IBM Technote—a technical document that addresses a specific technical issue.

Source: [The TechQA Dataset](https://arxiv.org/pdf/1911.02984v1.pdf)

Introducing paper: The TechQA Dataset
Refer360°
Full name: Refer360°

Refer360° is a novel large-scale referring expression recognition dataset consisting of 17,137 instruction sequences and ground-truth actions for completing these instructions in 360° scenes.

Source: [Refer360° : A Referring Expression Recognition Dataset in 360° Images](https://www.aclweb.org/anthology/2020.acl-main.644.pdf)

Introducing paper: Refer360$^\circ$: A Referring Expression Recognition Dataset in 360$^\circ$ Images
MUStARD
Full name: Multimodal Sarcasm Detection Dataset

We release the MUStARD dataset which is a multimodal video corpus for research in automated sarcasm discovery. The dataset is compiled from popular TV shows including Friends, The Golden Girls, The Big Bang Theory, and Sarcasmaholics Anonymous. MUStARD consists of audiovisual utterances annotated with sarcasm labels. Each utterance is accompanied by its context, which provides additional information on the scenario where the utterance occurs.

Source: [MUStARD](https://github.com/soujanyaporia/MUStARD)

Introducing paper: Towards Multimodal Sarcasm Detection (An \_Obviously\_ Perfect Paper)
ChID
Full name: Chinese IDiom dataset

ChID is a large-scale Chinese IDiom dataset for cloze test. ChID contains 581K passages and 729K blanks, and covers multiple domains. In ChID, the idioms in a passage were replaced with blank symbols. For each blank, a list of candidate idioms including the golden idiom are provided as choice. 

Source: [ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://arxiv.org/pdf/1906.01265v3.pdf)
Image Source: [https://arxiv.org/pdf/1906.01265v3.pdf](https://arxiv.org/pdf/1906.01265v3.pdf)

Introducing paper: ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
TalkSumm
Full name: TalkSumm

The **TalkSumm** dataset contains 1705 automatically-generated summaries of scientific papers from ACL, NAACL, EMNLP, SIGDIAL (2015-2018), and ICML (2017-2018).

The dataset is provided as a list of titles and URLs and the corresponding summaries.

Source: [GitHub](https://github.com/levguy/talksumm)

Introducing paper: TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks
CONAN
Full name: COunter NArratives through Nichesourcing

COunter NArratives through Nichesourcing (CONAN) is a dataset that consists of 4,078 pairs over the 3 languages. Additionally, 3 types of metadata are provided: expert demographics, hate speech sub-topic and counter-narrative type. The dataset is augmented through translation (from Italian/French to English) and paraphrasing, which brought the total number of pairs to 14.988.

Source: [CONAN](https://github.com/marcoguerini/CONAN)
Image Source: [https://www.aclweb.org/anthology/P19-1271](https://www.aclweb.org/anthology/P19-1271)

Introducing paper: CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech
VIST-Edit
Full name: VIST-Edit

The dataset, VIST-Edit, includes 14,905 human-edited versions of 2,981 machine-generated visual stories. The stories were generated by two state-of-the-art visual storytelling models, each aligned to 5 human-edited versions.

Source: [Visual Story Post-Editing](https://arxiv.org/pdf/1906.01764v1.pdf)

Introducing paper: Visual Story Post-Editing
OQGend
Full name: OQGend

Dataset OQRanD and OQGenD for paper "Asking the crowd: Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums" by Zi Chai, Xinyu Xing, Xiaojun Wan and Bo Huang. This paper is accepted by ACL'19.

The OQGenD dataset can be viewed at "OQGenD.xml". Each data (NQ-pairs) contains a certain piece of news with multiple related open-answered questions.

The OQRanD dataset can be viewed at "OQRanD.xml". Each data (Question Pairs) contains two questions, Q2 has more answers than Q1.

Source: [GitHub](https://github.com/ChaiZ-pku/OQRanD-and-OQGenD)

Introducing paper: Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums
OQRanD
Full name: OQRanD

Dataset OQRanD and OQGenD for paper "Asking the crowd: Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums" by Zi Chai, Xinyu Xing, Xiaojun Wan and Bo Huang. This paper is accepted by ACL'19.

The OQGenD dataset can be viewed at "OQGenD.xml". Each data (NQ-pairs) contains a certain piece of news with multiple related open-answered questions.

The OQRanD dataset can be viewed at "OQRanD.xml". Each data (Question Pairs) contains two questions, Q2 has more answers than Q1.

Source: [GitHub](https://github.com/ChaiZ-pku/OQRanD-and-OQGenD)

Introducing paper: Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums
LitBank
Full name: LitBank

LitBank is an annotated dataset of 100 works of English-language fiction to support tasks in natural language processing and the computational humanities, described in more detail in the following publications:

- David Bamman, Sejal Popat and Sheng Shen (2019), "An Annotated Dataset of Literary Entities," NAACL 2019.
- Matthew Sims, Jong Ho Park and David Bamman (2019), "Literary Event Detection," ACL 2019.
- David Bamman, Olivia Lewke and Anya Mansoor (2020), "An Annotated Dataset of Coreference in English Literature", LREC.

LitBank currently contains annotations for entities, events, entity coreference, and quotation attribution in a sample of ~2,000 words from each of those texts, totaling 210,532 tokens.

LitBank is licensed under a Creative Commons Attribution 4.0 International License.

Introducing paper: An annotated dataset of literary entities