{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:13:28Z","timestamp":1715645608109},"reference-count":59,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2023,9,1]]},"DOI":"10.1587\/transinf.2022edp7225","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T23:08:33Z","timestamp":1693523313000},"page":"1584-1599","source":"Crossref","is-referenced-by-count":0,"title":["Discriminative Question Answering via Cascade Prompt Learning and Sentence Level Attention Mechanism"],"prefix":"10.1587","volume":"E106.D","author":[{"given":"Xiaoguang","family":"YUAN","sequence":"first","affiliation":[{"name":"College of System Engineering, National University of Defense Technology"},{"name":"Beijing Institute of Computer Technology and Application"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaofan","family":"DAI","sequence":"additional","affiliation":[{"name":"College of System Engineering, National University of Defense Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongkai","family":"TIAN","sequence":"additional","affiliation":[{"name":"Beijing Institute of Computer Technology and Application"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"FAN","sequence":"additional","affiliation":[{"name":"Beijing Institute of Computer Technology and Application"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingyi","family":"SONG","sequence":"additional","affiliation":[{"name":"Beijing Institute of Computer Technology and Application"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengwen","family":"YU","sequence":"additional","affiliation":[{"name":"Beijing Institute of Computer Technology and Application"},{"name":"School of Computer Science and Technology, Xidian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"WANG","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"KE","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] P. Zweigenbaum, \u201cQuestion answering in biomedicine,\u201d Proc. EACL, 2003."},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] F. Zhu, W. Lei, Y. Huang, C. Wang, S. Zhang, J. Lv, F. Feng, and T.-S. Chua, \u201cTAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance,\u201d Proc. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp.3277-3287, Online, Association for Computational Linguistics, 2021. 10.18653\/v1\/2021.acl-long.254","DOI":"10.18653\/v1\/2021.acl-long.254"},{"key":"3","unstructured":"[3] S. Quarteroni and S. Manandhar, \u201cA chatbot-based interactive question answering system,\u201d Decalog 2007, 2007."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] M. Bulla, L. Hillebrand, M. L\u00fcbbering, and R. Sifa, \u201cKnowledge graph based question answering system for financial securities,\u201d German Conference on Artificial Intelligence (K\u00fcnstliche Intelligenz), pp.44-50, 2021. 10.1007\/978-3-030-87626-5_4","DOI":"10.1007\/978-3-030-87626-5_4"},{"key":"5","unstructured":"[5] C. Li, W. Ye, and Y. Zhao, \u201cFinmath: Injecting a tree-structured solver for question answering over financial reports,\u201d Proc. LREC, pp.6147-6152, 2022."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] G. Li and T. Zhao, \u201cApproach of intelligence question-answering system based on physical fitness knowledge graph,\u201d 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE), 2021. 10.1109\/rcae53607.2021.9638824","DOI":"10.1109\/RCAE53607.2021.9638824"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] Q. Liu, S. Jiang, Y. Wang, and S. Li, \u201cLiveQA: A question answering dataset over sports live,\u201d Proc. 19th Chinese National Conference on Computational Linguistics, Haikou, China, pp.1057-1067, Chinese Information Processing Society of China, 2020.","DOI":"10.1007\/978-3-030-63031-7_23"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Q. Jin, Z. Yuan, G. Xiong, Q. Yu, H. Ying, C. Tan, M. Chen, S. Huang, X. Liu, and S. Yu, \u201cBiomedical question answering: A survey of approaches and challenges,\u201d ACM Computing Surveys (CSUR), vol.55, no.2, Article No.35, 2022. 10.1145\/3490238","DOI":"10.1145\/3490238"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] Y. Gu, R. Tinn, H. Cheng, M. Lucas, N. Usuyama, X. Liu, T. Naumann, J. Gao, and H. Poon, \u201cDomain-specific language model pretraining for biomedical natural language processing,\u201d ACM Trans. Computing for Healthcare (HEALTH), vol.3, no,1, Article No.2, 2021. 10.1145\/3458754","DOI":"10.1145\/3458754"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, \u201cFreebase: A collaboratively created graph database for structuring human knowledge,\u201d Proc. SIGMOD, pp.1247-1250, 2008. 10.1145\/1376616.1376746","DOI":"10.1145\/1376616.1376746"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P.N. Mendes, S. Hellmann, M. Morsey, P. van Kleef, S. Auer, and C. Bizer, \u201cDBpedia \u2014 A large-scale, multilingual knowledge base extracted from Wikipedia,\u201d Semantic Web, vol.6, no.2, pp.167-195, 2015. 10.3233\/sw-140134","DOI":"10.3233\/SW-140134"},{"key":"12","unstructured":"[12] T.P. Tanon, D. Vrande\u010di\u0107, S. Schaffert, T. Steiner, and L. Pintscher, \u201cFrom freebase to wikidata: The great migration,\u201d Proc. 25th International Conference on World Wide Web, WWW 2016, pp.1419-1428, Montreal, Canada, April 11-15, 2016, ed. J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks, and B.Y. Zhao, ACM, 2016. 10.1145\/2872427.2874809"},{"key":"13","unstructured":"[13] Md.A. Karim, H. Ali, P. Das, M. Abdelwaheb, and S. Decker, \u201cQuestion answering over biological knowledge graph via amazon alexa,\u201d ArXiv preprint, arXiv:2210.06040, 2022. 10.48550\/arXiv.2210.06040"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] X. Zou, \u201cA survey on application of knowledge graph,\u201d Journal of Physics: Conference Series, vol.1487, 012016, 2020. 10.1088\/1742-6596\/1487\/1\/012016","DOI":"10.1088\/1742-6596\/1487\/1\/012016"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] A. Miller, A. Fisch, J. Dodge, A.-H. Karimi, A. Bordes, and J. Weston, \u201cKey-value memory networks for directly reading documents,\u201d Proc. 2016 Conference on Empirical Methods in Natural Language Processing, pp.1400-1409, Austin, Texas, Association for Computational Linguistics, 2016. 10.18653\/v1\/d16-1147","DOI":"10.18653\/v1\/D16-1147"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] P. Lewis, B. Oguz, R. Rinott, S. Riedel, and H. Schwenk, \u201cMLQA: Evaluating cross-lingual extractive question answering,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics, pp.7315-7330, Online, Association for Computational Linguistics, 2020. 10.18653\/v1\/2020.acl-main.653","DOI":"10.18653\/v1\/2020.acl-main.653"},{"key":"17","unstructured":"[17] P. Xu, D. Liang, Z. Huang, and B. Xiang, \u201cAttention-guided generative models for extractive question answering,\u201d ArXiv preprint, arXiv:2110.06393, 2021. 10.48550\/arXiv.2110.06393"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] X. Zhu, Y. Chen, Y. Gu, and Z. Xiao, \u201cSentiMedQAer: A transfer learning-based sentiment-aware model for biomedical question answering,\u201d Frontiers in Neurorobotics, vol.16, 2022. 10.3389\/fnbot.2022.773329","DOI":"10.3389\/fnbot.2022.773329"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] D. Khashabi, S. Min, T. Khot, A. Sabharwal, O. Tafjord, P. Clark, and H. Hajishirzi, \u201cUNIFIEDQA: Crossing format boundaries with a single QA system,\u201d Findings of the Association for Computational Linguistics: EMNLP 2020, pp.1896-1907, Online, Association for Computational Linguistics, 2020. 10.18653\/v1\/2020.findings-emnlp.171","DOI":"10.18653\/v1\/2020.findings-emnlp.171"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] M. Sarrouti and S.O. El Alaoui, \u201cA yes\/no answer generator based on sentiment-word scores in biomedical question answering,\u201d International Journal of Healthcare Information Systems and Informatics (IJHISI), vol.12, no.3, 2017. 10.4018\/ijhisi.2017070104","DOI":"10.4018\/IJHISI.2017070104"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] G. Salton and C. Buckley, \u201cTerm-weighting approaches in automatic text retrieval,\u201d Information Processing &amp; Management, vol.24, no.5, pp.513-523, 1988. 10.1016\/0306-4573(88)90021-0","DOI":"10.1016\/0306-4573(88)90021-0"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] S. Robertson, H. Zaragoza, \u201cThe probabilistic relevance framework: BM25 and beyond,\u201d Foundations and Trends\u00ae in Information Retrieval, vol.3, no.4, pp.333-389, 2009. 10.1561\/1500000019","DOI":"10.1561\/1500000019"},{"key":"23","unstructured":"[23] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp.4171-4186, Minneapolis, Minnesota, Association for Computational Linguistics, 2019. 10.18653\/v1\/N19-1423"},{"key":"24","unstructured":"[24] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, P.J. Liu, \u201cExploring the limits of transfer learning with a unified text-to-text transformer,\u201d J. Mach. Learn. Res., vol.21, no.1, pp.5485-5551, 2020."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] V. Karpukhin, B. Oguz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih, \u201cDense passage retrieval for open-domain question answering,\u201d Proc. 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.6769-6781, Online, Association for Computational Linguistics, 2020. 10.18653\/v1\/2020.emnlp-main.550","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"26","unstructured":"[26] W. Xiong, X. Li, S. Iyer, J. Du, P. Lewis, W.Y. Wang, Y. Mehdad, S. Yih, S. Riedel, D. Kiela, and B. Oguz, \u201cAnswering complex open-domain questions with multi-hop dense retrieval,\u201d Proc. ICLR, 2021."},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] Y. Mao, P. He, X. Liu, Y. Shen, J. Gao, J. Han, and W. Chen, \u201cGeneration-augmented retrieval for open-domain question answering,\u201d Proc. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp.4089-4100, Online, Association for Computational Linguistics, 2021. 10.18653\/v1\/2021.acl-long.316","DOI":"10.18653\/v1\/2021.acl-long.316"},{"key":"28","unstructured":"[28] T.B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D.M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, \u201cLanguage models are few-shot learners,\u201d Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, Dec. 6-12, 2020, virtual, ed. H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, 2020."},{"key":"29","unstructured":"[29] D. Bahdanau, K. Cho, and Y. Bengio, \u201cNeural machine translation by jointly learning to align and translate,\u201d 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, ed. Y. Bengio and Y. LeCun, 2015."},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] T. Luong, H. Pham, and C.D. Manning, \u201cEffective approaches to attention-based neural machine translation,\u201d Proc. 2015 Conference on Empirical Methods in Natural Language Processing, pp.1412-1421, Lisbon, Portugal, Association for Computational Linguistics, 2015. 10.18653\/v1\/d15-1166","DOI":"10.18653\/v1\/D15-1166"},{"key":"31","unstructured":"[31] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp.5998-6008, Dec. 4-9, 2017, Long Beach, CA, USA, ed. I. Guyon, U. von Luxburg, S. Bengio, H.M. Wallach, R. Fergus, S.V.N. Vishwanathan, and R. Garnett, 2017."},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] F. Petroni, T. Rockt\u00e4schel, S. Riedel, P. Lewis, A. Bakhtin, Y. Wu, and A. Miller, \u201cLanguage models as knowledge bases?,\u201d Proc. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp.2463-2473, Hong Kong, China, Association for Computational Linguistics, 2019. 10.18653\/v1\/d19-1250","DOI":"10.18653\/v1\/D19-1250"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] A. Roberts, C. Raffel, and N. Shazeer, \u201cHow much knowledge can you pack into the parameters of a language model?,\u201d Proc. 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.5418-5426, Online, Association for Computational Linguistics, 2020. 10.18653\/v1\/2020.emnlp-main.437","DOI":"10.18653\/v1\/2020.emnlp-main.437"},{"key":"34","unstructured":"[34] C. Clark, K. Lee, M.-W. Chang, T. Kwiatkowski, M. Collins, and K. Toutanova, \u201cBoolQ: Exploring the surprising difficulty of natural yes\/no questions,\u201d Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp.2924-2936, Minneapolis, Minnesota, Association for Computational Linguistics, 2019. 10.18653\/v1\/N19-1300"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. Parikh, C. Alberti, D. Epstein, I. Polosukhin, J. Devlin, K. Lee, K. Toutanova, L. Jones, M. Kelcey, M.-W. Chang, A.M. Dai, J. Uszkoreit, Q. Le, and S. Petrov, \u201cNatural questions: A benchmark for question answering research,\u201d Trans. Association for Computational Linguistics, vol.7, pp.453-466, 2019. 10.1162\/tacl_a_00276","DOI":"10.1162\/tacl_a_00276"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] M. Joshi, E. Choi, D. Weld, and L. Zettlemoyer, \u201cTriviaQA: A large scale distantly supervised challenge dataset for reading comprehension,\u201d Proc. 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.1601-1611, 2017. 10.18653\/v1\/p17-1147","DOI":"10.18653\/v1\/P17-1147"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, \u201cSQuAD: 100,000+ questions for machine comprehension of text,\u201d Proc. 2016 Conference on Empirical Methods in Natural Language Processing, pp.2383-2392, 2016. 10.18653\/v1\/d16-1264","DOI":"10.18653\/v1\/D16-1264"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] D. Chen, A. Fisch, J. Weston, and A. Bordes, \u201cReading wikipedia to answer open-domain questions,\u201d Proc. 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.1870-1879, Vancouver, Canada, Association for Computational Linguistics, 2017. 10.18653\/v1\/p17-1171","DOI":"10.18653\/v1\/P17-1171"},{"key":"39","unstructured":"[39] K.M. Hermann, T. Kocisk\u00fd, E. Grefenstette, L. Espeholt, W. Kay, M. Suleyman, and P. Blunsom, \u201cTeaching machines to read and comprehend,\u201d Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, pp.1693-1701, Dec. 7-12, 2015, Montreal, Quebec, Canada, ed. C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama, and R. Garnett, 2015."},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] D. Chen, J. Bolton, and C.D. Manning, \u201cA thorough examination of the CNN\/daily mail reading comprehension task,\u201d Proc. 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.2358-2367, Berlin, Germany, Association for Computational Linguistics, 2016. 10.18653\/v1\/p16-1223","DOI":"10.18653\/v1\/P16-1223"},{"key":"41","unstructured":"[41] P.S.H. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. K\u00fcttler, M. Lewis, W. Yih, T. Rockt\u00e4schel, S. Riedel, and D. Kiela, \u201cRetrieval-augmented generation for knowledge-intensive NLP tasks,\u201d Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, Dec. 6-12, 2020, virtual, ed. H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, 2020."},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, \u201cBART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics, pp.7871-7880, Online, Association for Computational Linguistics, 2020. 10.18653\/v1\/2020.acl-main.703","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] K. Lee, M.-W. Chang, and K. Toutanova, \u201cLatent retrieval for weakly supervised open domain question answering,\u201d Proc. 57th Annual Meeting of the Association for Computational Linguistics, pp.6086-6096, Florence, Italy, Association for Computational Linguistics, 2019. 10.18653\/v1\/p19-1612","DOI":"10.18653\/v1\/P19-1612"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[44] K. Nishida, I. Saito, A. Otsuka, H. Asano, and J. Tomita, \u201cRetrieve-and-read: Multi-task learning of information retrieval and reading comprehension,\u201d Proc. 27th ACM International Conference on Information and Knowledge Management, pp.647-656, 2018. 10.1145\/3269206.3271702","DOI":"10.1145\/3269206.3271702"},{"key":"45","doi-asserted-by":"publisher","unstructured":"[45] O. Khattab, C. Potts, and M. Zaharia, \u201cRelevance-guided supervision for openQA with ColBERT,\u201d Trans. Association for Computational Linguistics, vol.9, pp.929-944, 2021. 10.1162\/tacl_a_00405","DOI":"10.1162\/tacl_a_00405"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] O. Khattab and M. Zaharia, \u201cColBERT: Efficient and effective passage search via contextualized late interaction over BERT,\u201d Proc. 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, pp.39-48, Virtual Event, China, July 25-30, 2020, ed. J. Huang, Y. Chang, X. Cheng, J. Kamps, V. Murdock, J. Wen, and Y. Liu, ACM, 2020. 10.1145\/3397271.3401075","DOI":"10.1145\/3397271.3401075"},{"key":"47","doi-asserted-by":"publisher","unstructured":"[47] S. Zhang, H. Zhao, Y. Wu, Z. Zhang, X. Zhou, and X. Zhou, \u201cDCMN+: Dual co-matching network for multi-choice reading comprehension,\u201d Proc. AAAI Conference on Artificial Intelligence, vol.35, no.4, pp.9563-9570, 2020. 10.1609\/aaai.v34i05.6502","DOI":"10.1609\/aaai.v34i05.6502"},{"key":"48","doi-asserted-by":"publisher","unstructured":"[48] X. Xu, T. Tohti, and A. Hamdulla, \u201cSSIN: Sentence semantic interaction network for multi-choice reading comprehension,\u201d IEEE Access, vol.10, pp.113915-113922, 2022. 10.1109\/access.2022.3217479","DOI":"10.1109\/ACCESS.2022.3217479"},{"key":"49","unstructured":"[49] J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou, \u201cChain of thought prompting elicits reasoning in large language models,\u201d ArXiv preprint, arXiv:2201.11903v1, 2022. 10.48550\/arXiv.2201.11903"},{"key":"50","unstructured":"[50] Y. Qin, X. Wang, Y. Su, Y. Lin, N. Ding, Z. Liu, J. Li, L. Hou, P. Li, M. Sun, and J. Zhou, \u201cExploring low-dimensional intrinsic task subspace via prompt tuning,\u201d ArXiv preprint, arXiv:2110.07867v1, 2021. 10.48550\/arXiv.2110.07867"},{"key":"51","doi-asserted-by":"crossref","unstructured":"[51] Y. Zhao, H. Zhao, L. Shen, and Y. Zhao, \u201cLite unified modeling for discriminative reading comprehension,\u201d ArXiv preprint, arXive:2203.14103, 2022. 10.48550\/arXiv.2203.14103","DOI":"10.18653\/v1\/2022.acl-long.594"},{"key":"52","unstructured":"[52] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, \u201cRoBERTa: A robustly optimized BERT pretraining approach,\u201d ArXiv preprint, arXiv:1907.11692, 2019. 10.48550\/arXiv.1907.11692"},{"key":"53","unstructured":"[53] DataFountain, \u201cSemantic retrieval and intelligent question answering competition.\u201d [EB\/OL], 2022. https:\/\/www.datafountain.cn\/competitions\/567\/ranking?isRedance=0&amp;sch=1930"},{"key":"54","unstructured":"[54] C. Gormley and Z. Tong, Elasticsearch: The Definitive Guide: A Distributed Real-Time Search and Analytics Engine, O&apos;Reilly Media, 2015."},{"key":"55","unstructured":"[55] I. Loshchilov and F. Hutter, \u201cDecoupled weight decay regularization,\u201d 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019."},{"key":"56","doi-asserted-by":"crossref","unstructured":"[56] Q. Cao, H. Trivedi, A. Balasubramanian, and N. Balasubramanian, \u201cDeFormer: Decomposing pre-trained transformers for faster question answering,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics, Online, pp.4487-4497, Association for Computational Linguistics, 2020. 10.18653\/v1\/2020.acl-main.411","DOI":"10.18653\/v1\/2020.acl-main.411"},{"key":"57","doi-asserted-by":"crossref","unstructured":"[57] D. Khashabi, T. Khot, and A. Sabharwal, \u201cMore bang for your buck: Natural perturbation for robust question answering,\u201d Proc. 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.163-170, Online, Association for Computational Linguistics, 2020. 10.18653\/v1\/2020.emnlp-main.12","DOI":"10.18653\/v1\/2020.emnlp-main.12"},{"key":"58","unstructured":"[58] T. Kojima, S.S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, \u201cLarge language models are zero-shot reasoners,\u201d ArXiv preprint, arXiv:2205.11916, 2022. 10.48550\/arXiv.2205.11916"},{"key":"59","unstructured":"[59] X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, and D. Zhou, \u201cSelf-consistency improves chain of thought reasoning in language models,\u201d ArXiv preprint, arXiv:2203.11171, 2022. 10.48550\/arXiv.2203.11171"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/9\/E106.D_2022EDP7225\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T04:58:30Z","timestamp":1715576310000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/9\/E106.D_2022EDP7225\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":59,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2022edp7225","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,1]]},"article-number":"2022EDP7225"}}