[1] M. A. Calijorne Soares and F. S. Parreiras, “A Literature Review on Question Answering Techniques, Paradigms and Systems,” J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 6, pp. 635–646, 2020, doi: 10.1016/j.jksuci.2018.08.005.
[2] Y. Feldman and R. El-Yaniv, “Multi-hop Paragraph Retrieval for Open-domain Question Answering,” ACL 2019 - 57th Annu. Meet. Assoc. Comput. Linguist. Proc. Conf., pp. 2296–2309, 2020, doi: 10.18653/v1/p19-1222.M.B.A. Haghighat, “Biometrics for Cybersecurity and Unconstrained Environments”, Ph.D. Thesis, University of Miami, USA, 2016.
[3] R. Etezadi and M. Shamsfard, “PeCoQ: A Dataset for Persian Complex Question Answering over Knowledge Graph,” in 2020 11th International Conference on Information and Knowledge Technology (IKT), Dec. 2020, pp. 102–106. doi: 10.1109/IKT51791.2020.9345610.
[4] M. B. Sajadi, B. Minaei, and A. Hadian, “Farsbase: A cross-domain farsi knowledge graph.” SEMANTICS&Posters&Demos, 2018.
[5] H. Babaei Giglou, N. Beyranvand, R. Moradi, A. M. Salehoof, and S. Bibak, “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, Nov. 2022, pp. 59– 68. [Online]. Available: https://aclanthology.org/2022.nlp4dh-1.9
[6] K. Darvishi, N. Shahbodagh, Z. Abbasiantaeb, and S. Momtazi, “PQuAD: A Persian Question Answering Dataset,” Feb. 2022.
[7] N. Jamali, Y. Yaghoobzadeh, and H. Faili, “PerCQA: Persian Community Question Answering Dataset.” 2021.
[8] S. Ayoubi, https://github.com/sajjjadayobi/PersianQA.
[9] A. Ghafouri, H. Naderi, M. Aghajani, M. Firouzmandi, “IslamicPCQA: A Dataset for Persian Multi-hop Complex Question Answering in Islamic Text Resources.” 2023.
[10] Kazemi, J. Mozafari, and M. A. Nematbakhsh, “PersianQuAD: The Native Question Answering Dataset for the Persian Language,” IEEE Access, vol. 10, pp. 26045–26057, 2022, doi: 10.1109/ACCESS.2022.3157289.
[11] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pretraining of deep bidirectional transformers for language understanding,” in NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2019, vol. 1, pp. 4171–4186. [Online]. Available: https://github.com/tensorflow/tensor2tensor
[12] Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.” 2020.
[13] M. Farahani, M. Gharachorloo, M. Farahani, and M. Manthouri, “ParsBERT: Transformer-based Model for Persian Language Understanding,” Neural Process. Lett., vol. 53, no. 6, pp. 3831– 3847, 2021, doi: 10.1007/s11063-021-10528-4.
[14] N. Abadani, J. Mozafari, A. Fatemi, M. A. Nematbakhsh, and A. Kazemi, “ParSQuAD: Machine Translated SQuAD dataset for Persian Question Answering,” in 2021 7th International Conference on Web Research (ICWR), 2021, pp. 163–168. doi: 10.1109/ICWR51868.2021.9443126.
[15] Z. Yang et al., “Hotpotqa: A dataset for diverse, explainable multi-hop question answering,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 2020, pp. 2369–2380. doi: 10.18653/v1/d18-1259.
[16] A. Ghafouri, M. A. Abbasi, and H. Naderi, "AriaBERT: A pre-trained Persian BERT model for natural language understanding," preprint, Research Square, Nov. 2023. [Online]. Available: https://doi.org/10.21203/rs.3.rs-3558473/v1
[17] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer and V. Stoyanov, "Roberta: A robustly optimized bert pretraining approach." arXiv preprint arXiv:1907.11692, 2019.
[18] A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott, L. Zettlemoyer and V. Stoyanov, “Unsupervised cross-lingual representation learning at scale,” arXiv preprint arXiv:1911.02116, 2019.
[19] F. Feng, Y. Yang, D. Cer, N. Arivazhagan and W. Wang, “Language-agnostic BERT sentence embedding,” arXiv preprint arXiv:2007.01852, 2020.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). RoERTa: A robustly optimized ERT pretraining approach. arXiv
preprint arXiv:1907.11692.
[20] Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.” 2020.
