ParsiNLU: A Suite of Language Understanding Challenges for Persian

PARSINLU: A Suite of Language Understanding Challenges for Persian Daniel Khashabi1 Arman Cohan1 Siamak Shakeri2 Pedram Hosseini3 Pouya Pezeshkpour4 Malihe Alikhani5 Moin Aminnaseri6 Marzieh Bitaab7 Faeze Brahman8 Sarik Ghazarian9 Mozhdeh Gheini9 Arman Kabiri10 Rabeeh Karimi Mahabagdi11 Omid Memarrast12 Ahmadreza Mosallanezhad7 Erfan Noury13 Shahab Raji14 Mohammad Sadegh Rasooli15 Sepideh Sadeghi2 Erfan Sadeqi Azer2 Niloofar Safi Samghabadi16 Mahsa Shafaei17 Saber Sheybani18 Ali Tazarv4 Yadollah Yaghoobzadeh19 1Allen Institute for AI, USA 2Google, USA 3George Washington University, USA 4UC Irvine, USA 5University of Pittsburgh, USA 6TaskRabbit, USA 7Arizona State University, USA 8UC Santa Cruz, USA 9University of Southern California, USA 10IMRSV Data Labs, Canada 11EPFL, Switzerland 12University of Illinois - Chicago, USA 13University of Maryland Baltimore County, USA 14Rutgers University, USA 15University of Pennsylvania, USA 16Expedia Inc., USA 17University of Houston, USA 18Indiana University - Bloomington, USA 19Microsoft, Canada 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 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 lan- guage understanding tasks—reading compre- hension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by na- tive speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this bench- mark and compare them with human per- formance, which provides valuable insights into our ability to tackle natural language un- derstanding challenges in Persian. We hope PARSINLU fosters further research and ad- vances in Persian language understanding.1 1 Introduction In recent years, considerable progress has been made in building stronger NLU models, particu- 1https://git.io/JIuRO. ⋆ The point of view of the authors are their own and not attributable to the company they work for. larly supported by high-quality benchmarks (Bowman et al., 2015; Rajpurkar et al., 2016; Wang et al., 2019) for resourceful languages like English. However, in many other languages, such benchmarks remain scarce, unfortunately, stagnat- ing the progress towards language understanding in these languages. In this work, we focus on developing natural language understanding (NLU) benchmarks for Persian (also known as Farsi). This language has many attributes that make it distinct from other well-studied languages. In terms of script, Persian is similar to Semitic languages (e.g., Arabic). Lin- guistically, however, Persian is an Indo-European language (Masica, 1993) and thus distantly related to most of the languages of Europe as well as the northern part of the Indian subcontinent. Such at- tributes make Persian a unique case to study in terms of language technologies. Although Persian is a widely spoken language (Simons and Fennig, 2017), our ability …

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