| [1] |
Zhu, Fengbin and Lei, Wenqiang and Wang, Chao and Zheng, Jianming and Poria, Soujanya and Chua, Tat-Seng. Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering. arXiv preprint arXiv:2101.00774. 2021. [DOI ] |
| [2] |
An, Weijie and Chen, Qin and Yang, Yan and He, Liang. Knowledge Memory Based LSTM Model for Answer Selection. Neural Information Processing. 34--42, Springer International Publishing. 2017. [DOI ] |
| [3] |
Moravvej, Seyed Vahid and Mousavirad, Seyed Jalaleddin and Moghadam, Mahshid Helali and Saadatmand, Mehrdad. An LSTM-Based Plagiarism Detection via Attention Mechanism and a Population-Based Approach for Pre-training Parameters with Imbalanced Classes. International Conference on Neural Information Processing. 690--701, Springer. 2021. [DOI ] |
| [4] |
Wang, Mengqiu and Smith, Noah A. and Mitamura, Teruko. What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). https://aclanthology.org/D07-1003/. 22--32, 2007. |
| [5] |
Yih, Scott Wen-tau and Chang, Ming-Wei and Meek, Chris and Pastusiak, Andrzej. Question Answering Using Enhanced Lexical Semantic Models. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. https://aclanthology.org/P13-1171/. 1744--1753, 2013. |
| [6] |
Lauriola, Ivano and Moschitti, Alessandro. Answer Sentence Selection Using Local and Global Context in Transformer Models.. {ECIR} (1). 298--312, 2021. [DOI ] |
| [7] |
Bian, Weijie and Li, Si and Yang, Zhao and Chen, Guang and Lin, Zhiqing. A Compare-Aggregate Model with Dynamic-Clip Attention for Answer Selection. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1987--1990, ACM. 2017. [DOI ] |
| [8] |
Yu, Lei and Hermann, Karl Moritz and Blunsom, Phil and Pulman, Stephen. Deep Learning for Answer Sentence Selection. arXiv: 1412.1632. 2014. [DOI ] |
| [9] |
Feng, Minwei and Xiang, Bing and Glass, Michael R. and Wang, Lidan and Zhou, Bowen. Applying Deep Learning to Answer Selection: A Study and An Open Task. 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). 813--820, IEEE. 2015. [DOI ] |
| [10] |
Wang, Deguang and Liang, Ye and Ma, Hengrui and Xu, Fengqiang. Refined answer selection method with attentive bidirectional long short-term memory network and self-attention mechanism for intelligent medical service robot. Applied Sciences. Publisher: MDPI. 13(5): 3016, 2023. [DOI ] |
| [11] |
Mozafari, Jamshid and Fatemi, Afsaneh and Nematbakhsh, Mohammad Ali. BAS: An Answer Selection Method Using BERT Language Model. arXiv preprint arXiv:1911.01528. 28, 2019. [DOI ] |
| [12] |
Matsubara, Yoshitomo and Vu, Thuy and Moschitti, Alessandro. Reranking for Efficient Transformer-based Answer Selection. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1577--1580, ACM. 2020. [DOI ] |
| [13] |
Gu, Shengwei and Luo, Xiangfeng and Wang, Hao and Huang, Jing and Wei, Qin and Huang, Subin. Improving answer selection with global features. Expert Systems. Number: 1 Publisher: Wiley Online Library. 38(1): e12603, 2021. [DOI ] |
| [14] |
Tayyar Madabushi, Harish and Lee, Mark and Barnden, John. Integrating Question Classification and Deep Learning for improved Answer Selection. Proceedings of the 27th International Conference on Computational Linguistics. https://aclanthology.org/C18-1278/. 3283--3294, Association for Computational Linguistics. 2018. |
| [15] |
Wu, Weijing and Deng, Yang and Liang, Yuzhi and Lei, Kai. Answer Category-Aware Answer Selection for Question Answering. IEEE Access. 4: 9, IEEE. 2020. [DOI ] |
| [16] |
Mozafari, Jamshid and Fatemi, Afsaneh and Moradi, Parham. A Method For Answer Selection Using DistilBERT And Important Words. 2020 6th International Conference on Web Research (ICWR). 72--76, IEEE. 2020. [DOI ] |
| [17] |
Rao, Jinfeng and He, Hua and Lin, Jimmy. Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 1913--1916, ACM. 2016. [DOI ] |
| [18] |
Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805. arXiv: 1810.04805. 16, 2018. [DOI ] |
| [19] |
Lai, Tuan and Tran, Quan Hung and Bui, Trung and Kihara, Daisuke. A Gated Self-attention Memory Network for Answer Selection. arXiv:1909.09696. arXiv: 1909.09696. 2019. [DOI ] |
| [20] |
Laskar, Md Tahmid Rahman and Huang, Xiangji and Hoque, Enamul. Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task. Proceedings of The 12th Language Resources and Evaluation Conference. https://aclanthology.org/2020.lrec-1.676/. 5505--5514, 2020. |
| [21] |
Boreshban, Yasaman and Mirroshandel, Seyed Abolghasem. A novel question answering system for religious domain in Persian. Publisher: ELECTRONIC INDUSTRIES. 8(2): 73--88, 2017. [DOI ] |
| [22] |
Boreshban, Yasaman and Yousefinasab, Hamed and Mirroshandel, Seyed Abolghasem. Providing a Religious Corpus of Question Answering System in Persian. Signal and Data Processing. 15(1): 87--102, 2018. [DOI ] |
| [23] |
Tohidi, Nasim and Hasheminejad, Seyed Mohammad Hossein. MOQAS: Multi-objective question answering system. Journal of Intelligent and Fuzzy Systems. 36(4): 3495--3512, 2019. [DOI ] |
| [24] |
Moravvej, Seyed Vahid and Kahaki, Mohammad Javad Maleki and Sartakhti, Moein Salimi and Mirzaei, Abdolreza. A Method Based on Attention Mechanism using Bidirectional Long-Short Term Memory(BLSTM) for Question Answering. 2021 29th Iranian Conference on Electrical Engineering (ICEE). 460--464, 2021. [DOI ] |
| [25] |
Perevalov, Aleksandr and Both, Andreas. Heirarchical Expected Answer Type Classification for Question Answering. 20: 5, 2021. |
| [26] |
Farouk, Mamdouh and Ishizuka, Mitsuru and Bollegala, Danushka. Graph matching based semantic search engine. Research conference on metadata and semantics research. 89--100, Springer. 2018. [DOI ] |
| [27] |
Aliguliyev, Ramiz M.. A new sentence similarity measure and sentence based extractive technique for automatic text summarization. Expert Systems with Applications. 36(4): 7764--7772, 2009. [DOI ] |
| [28] |
De Boni, Marco and Manandhar, Suresh. The Use of Sentence Similarity as a Semantic Relevance Metric for Question Answering.. New Directions in Question Answering. https://aaai.org/papers/0024-SS03-07-024-the-use-of-sentence-similarity-as-a-semantic-relevance-metric-for-question-answering/. 138--144, 2003. |
| [29] |
Alzahrani, Salha M. and Salim, Naomie and Abraham, Ajith. Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 42(2): 133--149, 2011. [DOI ] |
| [30] |
Ferreira, Rafael and Lins, Rafael Dueire and Simske, Steven J. and Freitas, Fred and Riss, Marcelo. Assessing sentence similarity through lexical, syntactic and semantic analysis. Computer Speech and Language. 39: 1--28, 2016. [DOI ] |
| [31] |
Batanovic, Vuk and Bojic, Dragan. Using Part-of-Speech Tags as Deep-Syntax Indicators in Determining Short-Text Semantic Similarity. Computer Science and Information Systems. 12(1): 1--31, 2015. [DOI ] |
| [32] |
Loper, Edward and Bird, Steven. NLTK: The Natural Language Toolkit. arXiv:cs/0205028. 2002. [DOI ] |
| [33] |
Mohtaj, Salar and Roshanfekr, Behnam and Zafarian, Atefeh and Asghari, Habibollah. Parsivar: A Language Processing Toolkit for Persian. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). https://aclanthology.org/L18-1179/. 7, 2018. |
| [34] |
Peters, Matthew E. and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke. Deep contextualized word representations. arXiv:1802.05365. 2018. [DOI ] |
| [35] |
Maanijou, Reza and Mirroshandel, Seyedabolghasem. Degarbayan: Developing a Persian paraphrase corpus by crowdsourcing. The CSI Journal on Computing Science and Information Technology. 15(1): 22--30, 2017. [DOI ] |
| [36] |
Abadani, Negin and Mozafari, Jamshid and Fatemi, Afsaneh and Nematbakhsh, Mohamadali and Kazemi, Arefeh. ParSQuAD: Persian Question Answering Dataset based on Machine Translation of SQuAD 2.0. International Journal of Web Research. 4(1): 34--46, 2021. [DOI ] |
| [37] |
Wu, Yonghui and Schuster, Mike and Chen, Zhifeng and Le, Quoc V. and Norouzi, Mohammad and Macherey, Wolfgang and Krikun, Maxim and Cao, Yuan and Gao, Qin and Macherey, Klaus. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv preprint arXiv:1609.08144. https://arxiv.org/abs/1609.08144. 2016. |
| [38] |
Yang, Yi and Yih, Wen-tau and Meek, Christopher. WikiQA: A Challenge Dataset for Open-Domain Question Answering. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2013--2018, Association for Computational Linguistics. 2015. [DOI ] |
| [39] |
Kazemi, Arefeh and Mozafari, Jamshid and Nematbakhsh, Mohammad Ali. PersianQuAD: The Native Question Answering Dataset for the Persian Language. IEEE Access. 10: 26045--26057, 2022. [DOI ] |
| [40] |
Farahani, Mehrdad and Gharachorloo, Mohammad and Farahani, Marzieh and Manthouri, Mohammad. ParsBERT: Transformer-based Model for Persian Language Understanding. arXiv:2005.12515. arXiv: 2005.12515. 2020. [DOI ] |
| [41] |
Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692. 2019. [DOI ] |
|