{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:21:08Z","timestamp":1778343668478,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T00:00:00Z","timestamp":1708128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T00:00:00Z","timestamp":1708128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s13042-023-02086-z","type":"journal-article","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T13:01:55Z","timestamp":1708174915000},"page":"3157-3168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ConDA: state-based data augmentation for context-dependent text-to-SQL"],"prefix":"10.1007","volume":"15","author":[{"given":"Dingzirui","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longxu","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3907-0335","authenticated-orcid":false,"given":"Wanxiang","family":"Che","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinbo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingan","family":"Shang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuri","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,17]]},"reference":[{"key":"2086_CR1","doi-asserted-by":"crossref","unstructured":"Yu T, Zhang R, Yang K, Yasunaga M, Wang D, Li Z, Ma J, Li I, Yao Q, Roman S, Zhang Z, Radev D (2018) Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. arXiv:1809.08887","DOI":"10.18653\/v1\/D18-1425"},{"key":"2086_CR2","unstructured":"Zelle JM, Mooney RJ (1996) Learning to parse database queries using inductive logic programming. In: AAAI\/IAAI, Vol. 2"},{"key":"2086_CR3","doi-asserted-by":"crossref","unstructured":"Yu T, Zhang R, Er HY, Li S, Xue E, Pang B, Lin XV, Tan YC, Shi T, Li Z, Jiang Y, Yasunaga M, Shim S, Chen T, Fabbri A, Li Z, Chen L, Zhang Y, Dixit S, Zhang V, Xiong C, Socher R, Lasecki WS, Radev D (2019) CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases","DOI":"10.18653\/v1\/D19-1204"},{"key":"2086_CR4","doi-asserted-by":"crossref","unstructured":"Liu Q, Ye Z, Yu T, Song L, Blunsom P (2022) Augmenting multi-turn text-to-SQL datasets with self-play. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 5608\u20135620. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates. https:\/\/aclanthology.org\/2022.findings-emnlp.411","DOI":"10.18653\/v1\/2022.findings-emnlp.411"},{"key":"2086_CR5","doi-asserted-by":"crossref","unstructured":"Cai Z, Li X, Hui B, Yang M, Li B, Li B, Cao Z, Li W, Huang F, Si L, Li Y (2022) STAR: SQL guided pre-training for context-dependent text-to-SQL parsing. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 1235\u20131247. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates. https:\/\/aclanthology.org\/2022.findings-emnlp.89","DOI":"10.18653\/v1\/2022.findings-emnlp.89"},{"key":"2086_CR6","doi-asserted-by":"crossref","unstructured":"Yu T, Zhang R, Yasunaga M, Tan YC, Lin XV, Li S, Er H, Li I, Pang B, Chen T, Ji E, Dixit S, Proctor D, Shim S, Kraft J, Zhang V, Xiong C, Socher R, Radev D (2019) SParC: Cross-Domain Semantic Parsing in Context","DOI":"10.18653\/v1\/P19-1443"},{"key":"2086_CR7","unstructured":"Zelle JM, Mooney RJ (1996) Learning to parse database queries using inductive logic programming. In: Proceedings of the National Conference on Artificial Intelligence, pp. 1050\u20131055"},{"key":"2086_CR8","doi-asserted-by":"publisher","unstructured":"Wang B, Shin R, Liu X, Polozov O, Richardson M (2020) RAT-SQL: Relation-aware schema encoding and linking for text-to-SQL parsers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7567\u20137578. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.677. https:\/\/aclanthology.org\/2020.acl-main.677","DOI":"10.18653\/v1\/2020.acl-main.677"},{"key":"2086_CR9","doi-asserted-by":"publisher","unstructured":"Cai Y, Wan X (2020) IGSQL: Database schema interaction graph based neural model for context-dependent text-to-SQL generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6903\u20136912. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.560. https:\/\/aclanthology.org\/2020.emnlp-main.560","DOI":"10.18653\/v1\/2020.emnlp-main.560"},{"key":"2086_CR10","doi-asserted-by":"publisher","unstructured":"Scholak T, Schucher N, Bahdanau D (2021) PICARD: Parsing incrementally for constrained auto-regressive decoding from language models. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9895\u20139901. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.779. https:\/\/aclanthology.org\/2021.emnlp-main.779","DOI":"10.18653\/v1\/2021.emnlp-main.779"},{"key":"2086_CR11","doi-asserted-by":"crossref","unstructured":"Dou L, Gao Y, Pan M, Wang D, Lou J-G, Che W, Zhan D (2022) Unisar: A unified structure-aware autoregressive language model for text-to-sql. arXiv:2203.07781","DOI":"10.1007\/s13042-023-01898-3"},{"key":"2086_CR12","unstructured":"Rajkumar N, Li R, Bahdanau D (2022) Evaluating the Text-to-SQL Capabilities of Large Language Models"},{"key":"2086_CR13","unstructured":"Pourreza M, Rafiei D (2023) DIN-SQL: Decomposed in-context learning of text-to-SQL with self-correction. In: Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=p53QDxSIc5"},{"key":"2086_CR14","doi-asserted-by":"crossref","unstructured":"Chang S, Fosler-Lussier E (2023) Selective Demonstrations for Cross-domain Text-to-SQL","DOI":"10.18653\/v1\/2023.findings-emnlp.944"},{"key":"2086_CR15","doi-asserted-by":"publisher","unstructured":"Zhang R, Yu T, Er H, Shim S, Xue E, Lin XV, Shi T, Xiong C, Socher R, Radev D (2019) Editing-based SQL query generation for cross-domain context-dependent questions. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5338\u20135349. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1537. https:\/\/aclanthology.org\/D19-1537","DOI":"10.18653\/v1\/D19-1537"},{"key":"2086_CR16","doi-asserted-by":"crossref","unstructured":"Hui B, Geng R, Ren Q, Li B, Li Y, Sun J, Huang F, Si L, Zhu P, Zhu X (2021) Dynamic hybrid relation exploration network for cross-domain context-dependent semantic parsing. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, pp. 13116\u201313124. AAAI Press. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17550","DOI":"10.1609\/aaai.v35i14.17550"},{"key":"2086_CR17","doi-asserted-by":"crossref","unstructured":"Wang R, Ling Z, Zhou J, Hu Y (2021) Tracking interaction states for multi-turn text-to-sql semantic parsing. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pp. 13979\u201313987. AAAI Press, ???. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17646","DOI":"10.1609\/aaai.v35i16.17646"},{"key":"2086_CR18","doi-asserted-by":"crossref","unstructured":"Wu K, Wang L, Li Z, Zhang A, Xiao X, Wu H, Zhang M, Wang H (2021) Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing. arXiv:2103.02227","DOI":"10.18653\/v1\/2021.emnlp-main.707"},{"key":"2086_CR19","unstructured":"Yu T, Wu C-S, Lin XV, bailin wang, Tan YC, Yang X, Radev D, richard socher, Xiong C (2021) Grappa: Grammar-augmented pre-training for table semantic parsing. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=kyaIeYj4zZ"},{"key":"2086_CR20","unstructured":"Yu T, Zhang R, Polozov A, Meek C, Awadallah AH (2021) SCore: Pre-training for context representation in conversational semantic parsing. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=oyZxhRI2RiE"},{"key":"2086_CR21","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.aiopen.2022.03.001","volume":"3","author":"B Li","year":"2022","unstructured":"Li B, Hou Y, Che W (2022) Data augmentation approaches in natural language processing: a survey. AI Open 3:71\u201390. https:\/\/doi.org\/10.1016\/j.aiopen.2022.03.001","journal-title":"AI Open"},{"key":"2086_CR22","doi-asserted-by":"publisher","unstructured":"Barzilay R, McKeown KR (2001) Extracting paraphrases from a parallel corpus. In: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, pp. 50\u201357. Association for Computational Linguistics, Toulouse, France. https:\/\/doi.org\/10.3115\/1073012.1073020. https:\/\/aclanthology.org\/P01-1008","DOI":"10.3115\/1073012.1073020"},{"key":"2086_CR23","doi-asserted-by":"publisher","unstructured":"Sennrich R, Haddow B, Birch A (2016) Improving neural machine translation models with monolingual data. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 86\u201396. Association for Computational Linguistics, Berlin, Germany. https:\/\/doi.org\/10.18653\/v1\/P16-1009. https:\/\/aclanthology.org\/P16-1009","DOI":"10.18653\/v1\/P16-1009"},{"key":"2086_CR24","doi-asserted-by":"crossref","unstructured":"Liu Q, Kusner M, Blunsom P (2021) Counterfactual data augmentation for neural machine translation. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 187\u2013197","DOI":"10.18653\/v1\/2021.naacl-main.18"},{"key":"2086_CR25","doi-asserted-by":"publisher","unstructured":"Longpre S, Lu Y, Tu Z, DuBois C (2019) An exploration of data augmentation and sampling techniques for domain-agnostic question answering. In: Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pp. 220\u2013227. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-5829. https:\/\/aclanthology.org\/D19-5829","DOI":"10.18653\/v1\/D19-5829"},{"key":"2086_CR26","doi-asserted-by":"crossref","unstructured":"Jia R, Liang P (2016) Data Recombination for Neural Semantic Parsing. arXiv:1606.03622","DOI":"10.18653\/v1\/P16-1002"},{"key":"2086_CR27","unstructured":"Hou Y, Liu Y, Che W, Liu T (2018) Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding. arXiv:1807.01554"},{"key":"2086_CR28","unstructured":"Yu T, Wu C-S, Lin XV, Wang B, Tan YC, Yang X, Radev D, Socher R, Xiong C (2020) GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing. arXiv:2009.13845"},{"key":"2086_CR29","doi-asserted-by":"crossref","unstructured":"Zhong V, Lewis M, Wang SI, Zettlemoyer L (2021) Grounded Adaptation for Zero-shot Executable Semantic Parsing","DOI":"10.18653\/v1\/2020.emnlp-main.558"},{"key":"2086_CR30","doi-asserted-by":"crossref","unstructured":"Wang B, Yin W, Lin XV, Xiong C (2021) Learning to Synthesize Data for Semantic Parsing. arXiv:2104.05827","DOI":"10.18653\/v1\/2021.naacl-main.220"},{"key":"2086_CR31","doi-asserted-by":"publisher","unstructured":"Yang K, Deng O, Chen C, Shin R, Roy S, Van Durme B (2022) Addressing resource and privacy constraints in semantic parsing through data augmentation. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 3685\u20133695. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.291. https:\/\/aclanthology.org\/2022.findings-acl.291","DOI":"10.18653\/v1\/2022.findings-acl.291"},{"key":"2086_CR32","unstructured":"LI S, Yavuz S, Hashimoto K, Li J, Niu T, Rajani N, Yan X, Zhou Y, Xiong C (2021) Coco: Controllable counterfactuals for evaluating dialogue state trackers. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=eom0IUrF__F"},{"key":"2086_CR33","doi-asserted-by":"publisher","unstructured":"Yu T, Yasunaga M, Yang K, Zhang R, Wang D, Li Z, Radev D (2018) SyntaxSQLNet: Syntax tree networks for complex and cross-domain text-to-SQL task. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1653\u20131663. Association for Computational Linguistics, Brussels, Belgium. https:\/\/doi.org\/10.18653\/v1\/D18-1193. https:\/\/aclanthology.org\/D18-1193","DOI":"10.18653\/v1\/D18-1193"},{"key":"2086_CR34","doi-asserted-by":"crossref","unstructured":"Guo D, Sun Y, Tang D, Duan N, Yin J, Chi H, Cao J, Chen P, Zhou M (2018) Question Generation from SQL Queries Improves Neural Semantic Parsing. arXiv:1808.06304","DOI":"10.18653\/v1\/D18-1188"},{"key":"2086_CR35","doi-asserted-by":"publisher","unstructured":"Qin L, Xie T, Che W, Liu T. A survey on spoken language understanding: Recent advances and new frontiers. In: Zhou, Z.-H. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pp. 4577\u20134584. International Joint Conferences on Artificial Intelligence Organization. https:\/\/doi.org\/10.24963\/ijcai.2021\/622. Survey Track","DOI":"10.24963\/ijcai.2021\/622"},{"key":"2086_CR36","doi-asserted-by":"publisher","unstructured":"Liu Q, Yang D, Zhang J, Guo J, Zhou B, Lou J-G (2021) Awakening latent grounding from pretrained language models for semantic parsing. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1174\u20131189. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.100. https:\/\/aclanthology.org\/2021.findings-acl.100","DOI":"10.18653\/v1\/2021.findings-acl.100"},{"key":"2086_CR37","doi-asserted-by":"publisher","unstructured":"Yu T, Zhang R, Yasunaga M, Tan YC, Lin XV, Li S, Er H, Li I, Pang B, Chen T, Ji E, Dixit S, Proctor D, Shim S, Kraft J, Zhang V, Xiong C, Socher R, Radev D (2019) SParC: Cross-domain semantic parsing in context. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4511\u20134523. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1443. https:\/\/aclanthology.org\/P19-1443","DOI":"10.18653\/v1\/P19-1443"},{"key":"2086_CR38","doi-asserted-by":"publisher","unstructured":"Yu T, Zhang R, Er H, Li S, Xue E, Pang B, Lin XV, Tan YC, Shi T, Li Z, Jiang Y, Yasunaga M, Shim S, Chen T, Fabbri A, Li Z, Chen L, Zhang Y, Dixit S, Zhang V, Xiong C, Socher R, Lasecki W, Radev D (2019) CoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1962\u20131979. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1204. https:\/\/aclanthology.org\/D19-1204","DOI":"10.18653\/v1\/D19-1204"},{"key":"2086_CR39","doi-asserted-by":"publisher","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2020) BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.703. https:\/\/aclanthology.org\/2020.acl-main.703","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"2086_CR40","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2019) Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv:1910.10683"},{"key":"2086_CR41","doi-asserted-by":"crossref","unstructured":"Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, von Platen P, Ma C, Jernite Y, Plu J, Xu C, Scao TL, Gugger S, Drame M, Lhoest Q, Rush AM (2020) Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345. Association for Computational Linguistics, Online. https:\/\/www.aclweb.org\/anthology\/2020.emnlp-demos.6","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"2086_CR42","doi-asserted-by":"crossref","unstructured":"Ott M, Edunov S, Baevski A, Fan A, Gross S, Ng N, Grangier D, Auli M (2019) fairseq: A fast, extensible toolkit for sequence modeling. In: Proceedings of NAACL-HLT 2019: Demonstrations","DOI":"10.18653\/v1\/N19-4009"},{"key":"2086_CR43","doi-asserted-by":"crossref","unstructured":"Scholak T, Li R, Bahdanau D, de Vries H, Pal C (2020) DuoRAT: Towards Simpler Text-to-SQL Models. arXiv:2010.11119 [cs]","DOI":"10.18653\/v1\/2021.naacl-main.103"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02086-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02086-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02086-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T07:28:57Z","timestamp":1719991737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02086-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,17]]},"references-count":43,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["2086"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02086-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,17]]},"assertion":[{"value":"22 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}