The Winograd Schema Challenge was introduced both as an alternative to the Turing Test and as a test of a system’s ability to do commonsense reasoning. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes. The original Winograd Schema Challenge dataset consisted of 100 Winograd schemas constructed manually by AI experts. As of 2020 there are 285 examples available; however, the last 12 examples were only added recently. To ensure consistency with earlier models, several authors often prefer to report the performance on the first 273 examples only. These datasets are usually referred to as WSC285 and WSC273, respectively. Source: https://arxiv.org/pdf/2004.13831.pdf Image Source: https://arxiv.org/pdf/1907.11983.pdf

NewsCLIPpings is a dataset for detecting mismatched images and captions. Different to previous misinformation datasets, in NewsCLIPpings both the images and captions are unmanipulated, but some of them are mismatched.

Nepali News Corpus ## Raw nepali text scrapped from several online websites. ### Total file size of concatenated text file -> 4.6 GB. ## This raw text corpus can be used for tasks such as casual language modelling and masked language modelling.

The Tagalog Universal Dependencies NewsCrawl dataset consists of annotated text extracted from the Leipzig Tagalog Corpus. Data included in the Leipzig Tagalog Corpus were crawled from Tagalog-language online news sites by the Leipzig University Institute for Computer Science. The text data was automatically parsed and annotated by Angelina Aquino (University of the Philippines), and then manually corrected according the UD guidelines adapted for Tagalog by Elsie Marie Or (University of the Philippines), Maria Bardají Farré (University of Cologne), and Dr. Nikolaus Himmelmann (University of Cologne). Further verification and automated corrections were done by Lester James Miranda (Allen AI). Due to the source of the data, several typos, grammatical errors, incomplete sentences, and Tagalog-English code-mixing can be found in the dataset. Treebank structure - Train: 12495 sents, 286891 tokens - Dev: 1561 sents, 37045 tokens - Test: 1563 sents, 36974 tokens ###Acknowledgments Aside from the named persons in the previous section, the following also contributed to the project as manual annotators of the dataset: - Patricia Anne Asuncion - Paola Ellaine Luzon - Jenard Tricano - Mary Dianne Jamindang - Michael Wilson Rosero - Jim Bagano - Yeddah Joy Piedad - Farah Cunanan - Calen Manzano - Aien Gengania - Prince Heinreich Omang - Noah Cruz - Leila Ysabelle Suarez - Orlyn Joyce Esquivel - Andre Magpantay The annotation project was made possible by the Deutsche Forschungsgemeinschaft (DFG)-funded project titled "Information distribution and language structure - correlation of grammatical expressions of the noun/verb distinction and lexical information content in Tagalog, Indonesian and German." The DFG project team is composed of Dr. Nikolaus Himmelmann and Maria Bardají Farré from the University of Cologne, and Dr. Gerhard Heyer, Dr. Michael Richter, and Tariq Yousef from the Leipzig University.