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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.
Unifiedqa: Crossing format boundaries with a single qa system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
Earlier work this paper cites.
Pars-absa: an aspect-based sentiment analysis dataset for Persian
Taha Shangipour Ataei, Kamyar Darvishi, Soroush Javdan, Behrouz Minaei-Bidgoli, and Sauleh Eetemadi. 2019 · 1908
Earlier work this paper cites.
Korquad1. 0: Korean qa dataset for machine reading comprehension
Seungyoung Lim, Myungji Kim, and Jooyoul Lee. 2019 · 1909
Earlier work this paper cites.
Product quality assessment using opinion mining in persian online shopping
Fatemeh HosseinzadehBendarkheili, Rezvan MohammadiBaghmolaei, and Ali Ahmadi. 2019 · 1921
Earlier work this paper cites.
A coefficient of agreement for nominal scales
Jacob Cohen. 1960 · 1960
Earlier work this paper cites.
Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
Earlier work this paper cites.
The measurement of observer agreement for categorical data
J Richard Landis and Gary G Koch. 1977 · 1977
Earlier work this paper cites.
The indo-aryan languages
Colin P Masica. 1993 · 1993
Earlier work this paper cites.
Lexical query paraphrasing for document retrieval
Ingrid Zukerman and Bhavani Raskutti. 2002 · 2002
Earlier work this paper cites.
The role of the corpus in writing a grammar: An introduction to a software
Mahmood Bijankhan. 2004 · 2004
Earlier work this paper cites.
Deepsentipers: Novel deep learning models trained over proposed augmented persian sentiment corpus
Javad PourMostafa Roshan Sharami, Parsa Abbasi Sarabestani, and Seyed Abolghasem Mirroshandel. 2020 · 2004
Earlier work this paper cites.
The OPUS corpus - parallel and free:
Jörg Tiedemann and Lars Nygaard. 2004 · 2004
Earlier work this paper cites.
Parsbert: Transformer-based model for Persian language understanding
Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, and Mohammad Manthouri. 2020 · 2005
Earlier work this paper cites.
Improved statistical machine translation using paraphrases
Chris Callison-Burch, Philipp Koehn, and Miles Osborne. 2006 · 2006
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Answering the question you wish they had asked: The impact of paraphrasing for question answering
Pablo Duboue and Jennifer Chu-Carroll. 2006 · 2006
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MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
Shayne Longpre, Yi Lu, and Joachim Daiber. 2020 · 2007
Earlier work this paper cites.
Farstail: A Persian natural language inference dataset
Hossein Amirkhani, Mohammad Azari Jafari, Azadeh Amirak, Zohreh Pourjafari, Soroush Faridan Jahromi, and Zeinab Kouhkan. 2020 · 2009
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Improved language modeling for English-Persian statistical machine translation
Mahsa Mohaghegh, Abdolhossein Sarrafzadeh, and Tom Moir. 2010 · 2010
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Aspect-based sentiment analysis of movie reviews on discussion boards
Tun Thura Thet, Jin-Cheon Na, and Christopher SG Khoo. 2010 · 2010
Earlier work this paper cites.
Improving Persian-English statistical machine translation:experiments in domain adaptation
Mahsa Mohaghegh, Abdolhossein Sarrafzadeh, and Tom Moir. 2011 · 2011
Earlier work this paper cites.
Tep: Tehran english-Persian parallel corpus
Mohammad Taher Pilevar, Heshaam Faili, and Abdol Hamid Pilevar. 2011 · 2011
Earlier work this paper cites.
Sentiment analysis and opinion mining
Bing Liu. 2012 · 2012
Earlier work this paper cites.
Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
Earlier work this paper cites.
Recognizing Textual Entailment: Models and Applications
Ido Dagan, Dan Roth, Mark Sammons, and Fabio Massimo Zanzotto. 2013 · 2013
Cited alongside, same era.
Orthographic and morphological processing for Persian-to-English statistical machine translation
Mohammad Sadegh Rasooli, Ahmed El Kholy, and Nizar Habash. 2013 · 2013
Cited alongside, same era.
Mctest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013 · 2013
Cited alongside, same era.
Uppsala Persian dependency treebank annotation guidelines
Mojgan Seraji, Carina Jahani, Beáta Megyesi, and Joakim Nivre. 2013 · 2013
Cited alongside, same era.
The language demographics of amazon mechanical turk
Ellie Pavlick, Matt Post, Ann Irvine, Dmitry Kachaev, and Chris Callison-Burch. 2014 · 2014
Cited alongside, same era.
SemEval-2014 task 4: Aspect based sentiment analysis
Investigating evaluation of open-domain dialogue systems with human generated multiple references
Prakhar Gupta, Shikib Mehri, Tiancheng Zhao, Amy Pavel, Maxine Eskenazi, and Jeffrey P Bigham. 2019 · 2019
Later among the works it cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
Later among the works it cites.
Payma: A tagged corpus of Persian named entities
Mahsa Sadat Shahshahani, Mahdi Mohseni, Azadeh Shakery, and Heshaam Faili. 2019 · 2019
Later among the works it cites.
Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence
Chi Sun, Luyao Huang, and Xipeng Qiu. 2019 · 2019
Later among the works it cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
Later among the works it cites.
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Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014 · 2014
Cited alongside, same era.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Cited alongside, same era.
Parallel global voices: a collection of multilingual corpora with citizen media stories
Prokopis Prokopidis, Vassilis Papavassiliou, and Stelios Piperidis. 2016 · 2016
Cited alongside, same era.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Cited alongside, same era.
Ethnologue: languages of Asia
Gary F Simons and Charles D Fennig. 2017 · 2017
Cited alongside, same era.
Multiple instance learning networks for fine-grained sentiment analysis
Stefanos Angelidis and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Cited alongside, same era.
Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019 · 2019
Later among the works it cites.
Paws-x: A cross-lingual adversarial dataset for paraphrase identification
Yinfei Yang, Yuan Zhang, Chris Tar, and Jason Baldridge. 2019 · 2019
Later among the works it cites.
Optimizing annotation effort using active learning strategies: A sentiment analysis case study in Persian
Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, and Omid Momenzadeh. 2020 · 2020
Closest in time.
Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2020
Closest in time.
Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson. 2020 · 2020
Closest in time.
GLUECoS: An evaluation benchmark for code-switched NLP
Simran Khanuja, Sandipan Dandapat, Anirudh Srinivasan, Sunayana Sitaram, and Monojit Choudhury. 2020 · 2020
Closest in time.
Lscp: Enhanced large scale colloquial persian language understanding
Hadi Abdi Khojasteh, Ebrahim Ansari, and Mahdi Bohlouli. 2020 · 2020
Closest in time.
Departamento de nosotros: How machine translated corpora affects language models in mrc tasks
Maria Khvalchik and Mikhail Malkin. 2020 · 2020
Closest in time.
MLQA: Evaluating cross-lingual extractive question answering
Patrick Lewis, Barlas Oguz, Ruty Rinott, Sebastian Riedel, and Holger Schwenk. 2020 · 2020
Closest in time.
Xglue: A new benchmark datasetfor cross-lingual pre-training, understanding and generation
Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, et al. 2020 · 2020
Closest in time.
Xcopa: A multilingual dataset for causal commonsense reasoning
Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, Qianchu Liu, Ivan Vulić, and Anna Korhonen. 2020 · 2020
Closest in time.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Closest in time.
Russiansuperglue: A russian language understanding evaluation benchmark
Tatiana Shavrina, Alena Fenogenova, Emelyanov Anton, Denis Shevelev, Ekaterina Artemova, Valentin Malykh, Vladislav Mikhailov, Maria Tikhonova, Andrey Chertok, and Andrey Evlampiev. 2020 · 2020
Closest in time.
Clue: A chinese language understanding evaluation benchmark
Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, et al. 2020 · 2020
Closest in time.
Improving massively multilingual neural machine translation and zero-shot translation
Biao Zhang, Philip Williams, Ivan Titov, and Rico Sennrich. 2020 · 2020
Closest in time.
Gooaq: Open question answering with diverse answer types
Daniel Khashabi, Amos Ng, Tushar Khot, Ashish Sabharwal, Hannaneh Hajishirzi, and Chris Callison-Burch. 2021 · 2021
Closest in time.
mt5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021 · 2021
Closest in time.