{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:06:11Z","timestamp":1750309571618,"version":"3.41.0"},"reference-count":15,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>Today, one of the most important tasks in natural language processing is answering user questions. Especially, users' questions nowadays moved from simple questions to complex questions. In recent years, several question answering datasets have been produced for Persian language, but none of them support complex open-domain and explainable questions. In this article, the PersianMHQA dataset is introduced which is the first open-domain question answering dataset for complex questions based on the unstructured Persian Wikipedia encyclopedia. This dataset contains 7,000 complex questions and sentence-level supporting facts are provided for each question that allows question answering systems to explain the predictions. The questions in this dataset are diverse and explainable and are not limited to any previous knowledge base. Various types of complexity are provided in this dataset, and the questions are designed in such a way that answering them requires reasoning over more than one paragraph.<\/jats:p>","DOI":"10.1145\/3711826","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T11:20:47Z","timestamp":1736508047000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PersianMHQA: A Dataset for Open Domain Persian Multi-hop Question Answering Based on Wikipedia Encyclopedia"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2077-5586","authenticated-orcid":false,"given":"Mobina","family":"Taji","sequence":"first","affiliation":[{"name":"Computer Engineering, Iran University of Science and Technology, Tehran, Iran (the Islamic Republic of)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9828-085X","authenticated-orcid":false,"given":"Arash","family":"Ghafouri","sequence":"additional","affiliation":[{"name":"Computer Engineering, Iran University of Science and Technology, Tehran, Iran (the Islamic Republic of)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3296-8505","authenticated-orcid":false,"given":"Hasan","family":"Naderi","sequence":"additional","affiliation":[{"name":"Computer Engineering, Iran University of Science and Technology, Tehran, Iran (the Islamic Republic of)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9327-7345","authenticated-orcid":false,"given":"Behrouz","family":"Minaei-Bidgoli","sequence":"additional","affiliation":[{"name":"Computer Engineering, Iran University of Science and Technology, Tehran, Iran (the Islamic Republic of)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2018.08.005"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1222"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/IKT51791.2020.9345610"},{"key":"e_1_3_2_5_2","article-title":"FarsBase: A cross-domain Farsi knowledge graph","author":"Sajadi M. 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BERT: Pretraining of deep bidirectional transformers for language understanding. In NAACL HLT 2019 - Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1 4171\u20134186. Retrieved from https:\/\/github.com\/tensorflow\/tensor2tensor"},{"key":"e_1_3_2_10_2","unstructured":"Z. Lan M. Chen S. Goodman K. Gimpel P. Sharma and R. Soricut. 2019. ALBERT: A lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)."},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","unstructured":"A. Ghafouri H. Naderi M. Aghajani and M. Firouzmandi. 2023. IslamicPCQA: A dataset for Persian multi-hop complex question answering in Islamic text resources. arXiv preprint arXiv:2304.11664 (2023). DOI:10.48550\/ARXIV.2304.11664","DOI":"10.48550\/ARXIV.2304.11664"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3157289"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.18653\/v1\/2022.nlp4dh-1.9","volume-title":"Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities","author":"Babaei Giglou H.","year":"2022","unstructured":"H. Babaei Giglou, N. Beyranvand, R. Moradi, A. M. Salehoof, and S. Bibak. 2022. ParsSimpleQA: The Persian simple question answering dataset and system over knowledge graph. In Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities. 59\u201368. Retrieved from https:\/\/aclanthology.org\/2022.nlp4dh-1.9"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/d18-1259"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-021-10528-4"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","unstructured":"A. Conneau K. Khandelwal N. Goyal V. Chaudhary G. Wenzek F. Guzma\u0144 E. Grave M. Ott L. Zettlemoyer and V. Stoyanov. 2019. Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116 2019. 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