Developing Question Answering systems (QA) is one of the main goals in Artificial Intelligence. With the advent of Deep Learning (DL) techniques, QA systems have witnessed significant advances. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the QA task; most of them are exclusively in English. In order to address the need for a high-quality QA dataset in the Persian language, we present PersianQuAD, the native QA dataset for the Persian language. We create PersianQuAD in four steps: 1) Wikipedia article selection, 2) question-answer collection, 3) three-candidates test set preparation, and 4) Data Quality Monitoring. PersianQuAD consists of approximately 20,000 questions and answers made by native annotators on a set of Persian Wikipedia articles. The answer to each question is a segment of the corresponding article text. To better understand PersianQuAD and ensure its representativeness, we analyze PersianQuAD and show it contains questions of varying types and difficulties. We also present three versions of a deep learning-based QA system trained with PersianQuAD. Our best system achieves an F1 score of 82.97% which is comparable to that of QA systems on English SQuAD, made by the Stanford University. This shows that PersianQuAD performs well for training deep-learning-based QA systems. Human performance on PersianQuAD is significantly better (96.49%), demonstrating that PersianQuAD is challenging enough and there is still plenty of room for future improvement. PersianQuAD and all QA models implemented in this paper are freely available. 

PersianQuAD: The Native Question Answering Dataset for the Persian Language

This paper describes the creation of Exa Persian Paraphrase Corpus (ExaPPC), a large paraphrase corpus consisting of monolingual sentence-level paraphrases using different sources. ExaPPC is the first large-scale paraphrase dataset used in Persian paraphrase detection to the best of our knowledge. There are 2.3M labeled sentence pairs in the corpus consisting of a 1M paraphrase label and 1.3M non-paraphrase label. Efforts were made manually and semi-automatically to construct this corpus using techniques such as subtitle alignment, translating existing parallel English-Persian corpus and similarity corpus on English tweets. In addition to enriching the corpus, candidate sentence pairs among tweets have been extracted via NLP tools and labeled by two Persian native speakers. The advantages of this corpus compared to the existing ones are the number of pair sentences, sentence Length variation and textual diversity, including formal and dialogue sentences. The result on the provided test corpus shows that ExaPPC achieves 94% accuracy on paraphrase detection task. The corpus is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/exaco/exappc 

Rabeeh Karimi Mahabagdi

Papers

ParsiNLU: A Suite of Language Understanding Challenges for Persian