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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 ParsiNLU: A Suite of Language Understanding Challenges for Persian ... Khashabi, Daniel; Cohan, Arman; Shakeri, Siamak; Hosseini, Pedram; Pezeshkpour, Pouya; Alikhani, Malihe; Aminnaseri, Moin; Bitaab, Marzieh; Brahman, Faeze; Ghazarian, Sarik; Gheini, Mozhdeh; Kabiri, Arman; Mahabadi, Rabeeh Karimi; Memarrast, Omid; Mosallanezhad, Ahmadreza; Noury, Erfan; Raji, Shahab; Rasooli, Mohammad Sadegh; Sadeghi, Sepideh; Azer, Erfan Sadeqi; Samghabadi, Niloofar Safi; Shafaei, Mahsa; Sheybani, Saber; Tazarv, Ali; Yaghoobzadeh, Yadollah. - : arXiv, 2020 Abstract: Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this rich language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of high-level tasks -- Reading Comprehension, Textual Entailment, etc. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5$k$ new instances across 6 distinct NLU tasks. Besides, we present the first results on state-of-the-art monolingual and multi-lingual pre-trained language-models on this benchmark and compare them with human performance, which ... : To appear on Transactions of the Association for Computational Linguistics (TACL), 2021 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2012.06154 https://arxiv.org/abs/2012.06154 BASE Hide details

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