{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:58Z","timestamp":1740108058355,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1908216"],"award-info":[{"award-number":["U1908216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2020AAA0108600"],"award-info":[{"award-number":["2020AAA0108600"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s00521-023-08795-4","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T05:01:37Z","timestamp":1696568497000},"page":"259-271","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An efficient confusing choices decoupling framework for multi-choice tasks over texts"],"prefix":"10.1007","volume":"36","author":[{"given":"Yingyao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junwei","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoqun","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youzheng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3132-3059","authenticated-orcid":false,"given":"Conghui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiejun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"8795_CR1","doi-asserted-by":"publisher","unstructured":"Su X, Wang R, Dai X (2022) Contrastive learning-enhanced nearest neighbor mechanism for multi-label text classification. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 2: short papers), pp 672\u2013679. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-short.75","DOI":"10.18653\/v1\/2022.acl-short.75"},{"key":"8795_CR2","doi-asserted-by":"publisher","unstructured":"Aly R, Remus S, Biemann C (2019) Hierarchical multi-label classification of text with capsule networks. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, pp 323\u2013330. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-2045","DOI":"10.18653\/v1\/P19-2045"},{"key":"8795_CR3","doi-asserted-by":"publisher","unstructured":"Ray Chowdhury J, Caragea C, Caragea D (2020) Cross-lingual disaster-related multi-label tweet classification with manifold mixup. In: Proceedings of the 58th annual meeting of the association for computational linguistics: student research workshop, pp 292\u2013298. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-srw.39","DOI":"10.18653\/v1\/2020.acl-srw.39"},{"key":"8795_CR4","doi-asserted-by":"publisher","unstructured":"Raina V, Gales M (2022) Answer uncertainty and unanswerability in multiple-choice machine reading comprehension. In: Findings of the association for computational linguistics: ACL 2022, pp 1020\u20131034. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.82","DOI":"10.18653\/v1\/2022.findings-acl.82"},{"key":"8795_CR5","doi-asserted-by":"publisher","unstructured":"Wang S, Yu M, Jiang J, Chang S (2018) A co-matching model for multi-choice reading comprehension. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 2: Short Papers), pp 746\u2013751. Association for Computational Linguistics, Melbourne, Australi. https:\/\/doi.org\/10.18653\/v1\/P18-2118","DOI":"10.18653\/v1\/P18-2118"},{"key":"8795_CR6","doi-asserted-by":"crossref","unstructured":"Kumar S (2022) Answer-level calibration for free-form multiple choice question answering. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long Papers), pp 665\u2013679","DOI":"10.18653\/v1\/2022.acl-long.49"},{"key":"8795_CR7","doi-asserted-by":"crossref","unstructured":"Alt C, H\u00fcbner M, Hennig L (2019) Fine-tuning pre-trained transformer language models to distantly supervised relation extraction. In: Proceedings of the 57th annual meeting of the association for computational linguistics","DOI":"10.18653\/v1\/P19-1134"},{"key":"8795_CR8","doi-asserted-by":"crossref","unstructured":"Tan Q, He R, Bing L, Ng HT (2022) Document-level relation extraction with adaptive focal loss and knowledge distillation","DOI":"10.18653\/v1\/2022.findings-acl.132"},{"key":"8795_CR9","doi-asserted-by":"crossref","unstructured":"Yang S, Zhang Y, Niu G, Zhao Q, Pu S (2021) Entity concept-enhanced few-shot relation extraction. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 2: short papers)","DOI":"10.18653\/v1\/2021.acl-short.124"},{"key":"8795_CR10","unstructured":"Ming YA, Yi PB (2021) Meta-learning for compressed language model: a multiple choice question answering study"},{"key":"8795_CR11","doi-asserted-by":"crossref","unstructured":"Liu Z, Huang K, Huang D, Zhao J (2020) Dual head-wise coattention network for machine comprehension with multiple-choice questions. In: CIKM \u201920: the 29th ACM international conference on information and knowledge management","DOI":"10.1145\/3340531.3412013"},{"key":"8795_CR12","doi-asserted-by":"crossref","unstructured":"Han X, Zhu H, Yu P, Wang Z, Yao Y, Liu Z, Sun M (2018) Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Proceedings of the 2018 conference on empirical methods in natural language processing","DOI":"10.18653\/v1\/D18-1514"},{"key":"8795_CR13","doi-asserted-by":"crossref","unstructured":"Lai G, Xie Q, Liu H, Yang Y, Hovy E (2017) Race: Large-scale reading comprehension dataset from examinations. In: Proceedings of the 2017 conference on empirical methods in natural language processing","DOI":"10.18653\/v1\/D17-1082"},{"key":"8795_CR14","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"issue":"1","key":"8795_CR15","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79\u201386","journal-title":"Ann Math Stat"},{"key":"8795_CR16","doi-asserted-by":"crossref","unstructured":"Wang Y, Bao J, Liu G, Wu Y, He X, Zhou B, Zhao T (2020) Learning to decouple relations: Few-shot relation classification with entity-guided attention and confusion-aware training. arXiv preprint arXiv:2010.10894","DOI":"10.18653\/v1\/2020.coling-main.510"},{"key":"8795_CR17","doi-asserted-by":"crossref","unstructured":"Ju Y, Zhang Y, Tian Z, Liu K, Cao X, Zhao W, Li J, Zhao J (2021) Enhancing multiple-choice machine reading comprehension by punishing illogical interpretations. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 3641\u20133652","DOI":"10.18653\/v1\/2021.emnlp-main.295"},{"key":"8795_CR18","doi-asserted-by":"crossref","unstructured":"Le Berre G, Cerisara C, Langlais P, Lapalme G (2022) Unsupervised multiple-choice question generation for out-of-domain Q &A fine-tuning. In: 60th annual meeting of the association for computational linguistics","DOI":"10.18653\/v1\/2022.acl-short.83"},{"key":"8795_CR19","doi-asserted-by":"crossref","unstructured":"Cho YM, Zhang L, Callison-Burch C (2022) Unsupervised entity linking with guided summarization and multiple-choice selection. In: Proceedings of the 2022 conference on empirical methods in natural language processing, pp 9394\u20139401","DOI":"10.18653\/v1\/2022.emnlp-main.638"},{"key":"8795_CR20","unstructured":"Pal A, Umapathi L.K, Sankarasubbu M (2022) Medmcqa: a large-scale multi-subject multi-choice dataset for medical domain question answering. In: Conference on health, inference, and learning, pp 248\u2013260. PMLR"},{"key":"8795_CR21","doi-asserted-by":"crossref","unstructured":"Zhuang Y, Li Y, Cheung JJ, Yu Y, Mou Y, Chen X, Song L, Zhang C (2022) Resel: N-ary relation extraction from scientific text and tables by learning to retrieve and select. arXiv preprint arXiv:2210.14427","DOI":"10.18653\/v1\/2022.emnlp-main.46"},{"key":"8795_CR22","doi-asserted-by":"crossref","unstructured":"Boroujeni G.A, Faili H, Yaghoobzadeh Y (2022) Answer selection in community question answering exploiting knowledge graph and context information. Semantic Web (Preprint), pp 1\u201318","DOI":"10.3233\/SW-222970"},{"key":"8795_CR23","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst, pp 5998\u20136008"},{"key":"8795_CR24","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: a lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942"},{"key":"8795_CR25","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"key":"8795_CR26","doi-asserted-by":"crossref","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2019) Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"8795_CR27","unstructured":"Benedetto L, Aradelli G, Cremonesi P, Cappelli A, Giussani A, Turrin R (2021) On the application of transformers for estimating the difficulty of multiple-choice questions from text. In: Proceedings of the 16th workshop on innovative use of NLP for building educational applications, pp 147\u2013157"},{"key":"8795_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106321","volume":"206","author":"W Huang","year":"2020","unstructured":"Huang W, Mao Y, Yang Z, Zhu L, Long J (2020) Relation classification via knowledge graph enhanced transformer encoder. Knowl-Based Syst 206:106321","journal-title":"Knowl-Based Syst"},{"key":"8795_CR29","doi-asserted-by":"crossref","unstructured":"Koshy R, Elango S (2022) Multimodal tweet classification in disaster response systems using transformer-based bidirectional attention model. Neural Comput Appl, pp 1\u201321","DOI":"10.1007\/s00521-022-07790-5"},{"key":"8795_CR30","doi-asserted-by":"publisher","unstructured":"Fale\u0144ska A, Kuhn J (2019) The (non-)utility of structural features in bilstm-based dependency parsers, pp 117\u2013128 . https:\/\/doi.org\/10.18653\/v1\/P19-1012","DOI":"10.18653\/v1\/P19-1012"},{"key":"8795_CR31","doi-asserted-by":"crossref","unstructured":"Ma N, Mazumder S, Wang H, Liu B (2020) Entity-aware dependency-based deep graph attention network for comparative preference classification. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 5782\u20135788","DOI":"10.18653\/v1\/2020.acl-main.512"},{"key":"8795_CR32","doi-asserted-by":"crossref","unstructured":"Chen K, Zhao T, Yang M, Liu L (2017) Translation prediction with source dependency-based context representation","DOI":"10.1609\/aaai.v31i1.10978"},{"key":"8795_CR33","doi-asserted-by":"crossref","unstructured":"Tang H, Ji D, Li C, Zhou Q (2020) Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 6578\u20136588","DOI":"10.18653\/v1\/2020.acl-main.588"},{"key":"8795_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103893","volume":"122","author":"V Kanjirangat","year":"2021","unstructured":"Kanjirangat V, Rinaldi F (2021) Enhancing biomedical relation extraction with transformer models using shortest dependency path features and triplet information. J Biomed Inf 122:103893","journal-title":"J Biomed Inf"},{"key":"8795_CR35","doi-asserted-by":"publisher","unstructured":"Jia W, Dai D, Xiao X, Wu H (2019) ARNOR: Attention regularization based noise reduction for distant supervision relation classification. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1399\u20131408. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1135","DOI":"10.18653\/v1\/P19-1135"},{"key":"8795_CR36","doi-asserted-by":"crossref","unstructured":"Dou C, Wu S, Zhang X, Feng Z, Wang K (2022) Function-words adaptively enhanced attention networks for few-shot inverse relation classification. In: Proceedings of the thirty-first international joint conference on artificial intelligence, pp 2937\u20132943","DOI":"10.24963\/ijcai.2022\/407"},{"key":"8795_CR37","unstructured":"Yu T, Yang M, Zhao X (2022) Dependency-aware prototype learning for few-shot relation classification. In: Proceedings of the 29th international conference on computational linguistics, pp 2339\u20132345. International committee on computational linguistics, Gyeongju, Republic of Korea. https:\/\/aclanthology.org\/2022.coling-1.205"},{"key":"8795_CR38","unstructured":"Xiao Y, Jin Y, Hao K (2021) Adaptive prototypical networks with label words and joint representation learning for few-shot relation classification. IEEE Trans Neural Netw Learn Syst"},{"key":"8795_CR39","doi-asserted-by":"publisher","unstructured":"Han X, Zhu H, Yu P, Wang Z, Yao Y, Liu Z, Sun M (2018) FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 4803\u20134809. Association for Computational Linguistics, Brussels, Belgium. https:\/\/doi.org\/10.18653\/v1\/D18-1514","DOI":"10.18653\/v1\/D18-1514"},{"key":"8795_CR40","doi-asserted-by":"crossref","unstructured":"Gao T, Han X, Liu Z, Sun M (2019) Hybrid attention-based prototypical networks for noisy few-shot relation classification. In: Proceedings of the thirty-second AAAI conference on artificial intelligence,(AAAI-19), New York, USA","DOI":"10.1609\/aaai.v33i01.33016407"},{"key":"8795_CR41","doi-asserted-by":"publisher","unstructured":"Ye Z.-X, Ling Z.-H (2019) Multi-level matching and aggregation network for few-shot relation classification. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 2872\u20132881. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1277","DOI":"10.18653\/v1\/P19-1277"},{"key":"8795_CR42","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst, pp 4077\u20134087"},{"key":"8795_CR43","doi-asserted-by":"crossref","unstructured":"Gao T, Han X, Zhu H, Liu Z, Li P, Sun M, Zhou J (2019) Fewrel 2.0: Towards more challenging few-shot relation classification. arXiv preprint arXiv:1910.07124","DOI":"10.18653\/v1\/D19-1649"},{"key":"8795_CR44","unstructured":"Soares L.B, FitzGerald N, Ling J, Kwiatkowski T (2019) Matching the blanks: Distributional similarity for relation learning. In: Proceedings of the 57th annual meeting of the association for computational linguistics"},{"key":"8795_CR45","doi-asserted-by":"crossref","unstructured":"Dong B, Yao Y, Xie R, Gao T, Han X, Liu Z, Lin F, Lin L, Sun M (2020) Meta-information guided meta-learning for few-shot relation classification. In: Proceedings of the 28th international conference on computational linguistics, pp 1594\u20131605","DOI":"10.18653\/v1\/2020.coling-main.140"},{"key":"8795_CR46","doi-asserted-by":"crossref","unstructured":"Ren S, Zhu KQ (2020) Knowledge-driven distractor generation for cloze-style multiple choice questions","DOI":"10.1609\/aaai.v35i5.16559"},{"key":"8795_CR47","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.neucom.2021.01.148","volume":"487","author":"M Yan","year":"2022","unstructured":"Yan M, Pan Y (2022) Meta-learning for compressed language model: a multiple choice question answering study. Neurocomputing 487:181\u2013189","journal-title":"Neurocomputing"},{"key":"8795_CR48","doi-asserted-by":"crossref","unstructured":"Manakul P, Liusie A, Gales MJ (2023) Mqag: multiple-choice question answering and generation for assessing information consistency in summarization. arXiv preprint arXiv:2301.12307","DOI":"10.18653\/v1\/2023.ijcnlp-main.4"},{"key":"8795_CR49","doi-asserted-by":"crossref","unstructured":"Wang S, Mo Y, Jing J, Chang S (2018) A co-matching model for multi-choice reading comprehension. In: Proceedings of the 56th annual meeting of the association for computational linguistics (volume 2: short papers)","DOI":"10.18653\/v1\/P18-2118"},{"key":"8795_CR50","doi-asserted-by":"crossref","unstructured":"Parikh S, Sai AB, Nema P, Khapra MM (2019) Eliminet: a model for eliminating options for reading comprehension with multiple choice questions","DOI":"10.24963\/ijcai.2018\/594"},{"key":"8795_CR51","unstructured":"Zhang S, Hai Z, Wu Y, Zhang Z, Xiang Z (2019) Dual co-matching network for multi-choice reading comprehension"},{"key":"8795_CR52","doi-asserted-by":"crossref","unstructured":"Tang M, Cai J, Zhuo HH (2019) Multi-matching network for multiple choice reading comprehension, pp 7088\u20137095","DOI":"10.1609\/aaai.v33i01.33017088"},{"key":"8795_CR53","doi-asserted-by":"crossref","unstructured":"Chen Z, Cui Y, Ma W, Wang S, Hu G (2019) Convolutional spatial attention model for reading comprehension with multiple-choice questions. In: Proceedings of the AAAI conference on artificial intelligence vol 33, pp 6276\u20136283","DOI":"10.1609\/aaai.v33i01.33016276"},{"key":"8795_CR54","doi-asserted-by":"crossref","unstructured":"Xie T, Wu CH, Shi P, Zhong R, Scholak T, Yasunaga M, Wu C-S, Zhong M, Yin P, Wang SI, et al (2022) Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models. arXiv preprint arXiv:2201.05966","DOI":"10.18653\/v1\/2022.emnlp-main.39"},{"key":"8795_CR55","doi-asserted-by":"crossref","unstructured":"Khashabi D, Min S, Khot T, Sabharwal A, Tafjord O, Clark P, Hajishirzi H (2020) Unifiedqa: Crossing format boundaries with a single qa system. arXiv preprint arXiv:2005.00700","DOI":"10.18653\/v1\/2020.findings-emnlp.171"},{"key":"8795_CR56","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A.N, Kaiser L, Polosukhin I (2017) Attention is all you need. https:\/\/arxiv.org\/pdf\/1706.03762.pdf"},{"key":"8795_CR57","doi-asserted-by":"crossref","unstructured":"Mintz M, Bills S, Snow R, Jurafsky D (2009) Distant supervision for relation extraction without labeled data. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: volume 2, pp 1003\u20131011. Association for Computational Linguistics","DOI":"10.3115\/1690219.1690287"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08795-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08795-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08795-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T04:54:33Z","timestamp":1730264073000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08795-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":57,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["8795"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08795-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,10,6]]},"assertion":[{"value":"15 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This paper has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The content of the paper is written in accordance with common ethical standards in the scientific community.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All the authors have given their consent to participate.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All the authors give consent to publish the paper.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}