Omkar2118/datasets huggingface dataset:
Long Video Dataset

We randomly selected three videos from the Internet, that are longer than 1.5K frames and have their main objects continuously appearing. Each video has 20 uniformly sampled frames manually annotated for evaluation.

Introducing paper: Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
Long Video Dataset (3X)

We randomly selected three videos from the Internet, that are longer than 1.5K frames and have their main objects continuously appearing. Each video has 20 uniformly sampled frames manually annotated for evaluation. Each video has been played back and forth to generate videos that are three times as long.

Introducing paper: Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement
WinoNB

This dataset consists of Winograd schemas that test coreference resolution systems' ability to differentiate singular vs plural they/them pronouns. It consists of 4077 templates, each with a group of people, a singular person (which can be filled with a name or a generic "someone") and a single they/them pronoun to resolve.

Introducing paper: Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution
iSarcasmEval

iSarcasmEval is the first shared task to target intended sarcasm detection: the data for this task was provided and labelled by the authors of the texts themselves. Such an approach minimises the downfalls of other methods to collect sarcasm data, which rely on distant supervision or third-party annotations. The shared task contains two languages, English and Arabic, and three subtasks: sarcasm detection, sarcasm category classification, and pairwise sarcasm identification given a sarcastic sentence and its non-sarcastic rephrase. The task received submissions from 60 different teams, with the sarcasm detection task being the most popular. Most of the participating teams utilised pre-trained language models. In this paper, we provide an overview of the task, data, and participating teams.

Introducing paper: SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic
ArSarcasm

ArSarcasm is a new Arabic sarcasm detection dataset. The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic.

Introducing paper: From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset
ArSarcasm-v2

ArSarcasm-v2 is an extension of the original ArSarcasm dataset published along with the paper From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset. ArSarcasm-v2 conisists of ArSarcasm along with portions of DAICT corpus and some new tweets. Each tweet was annotated for sarcasm, sentiment and dialect. The final dataset consists of 15,548 tweets divided into 12,548 training tweets and 3,000 testing tweets. ArSarcasm-v2 was used and released as a part of the shared task on sarcasm detection and sentiment analysis in Arabic.

Introducing paper: Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic