{"id":"https://openalex.org/W2886983157","doi":"https://doi.org/10.18653/v1/d18-1188","title":"Question Generation from SQL Queries Improves Neural Semantic Parsing","display_name":"Question Generation from SQL Queries Improves Neural Semantic Parsing","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2886983157","doi":"https://doi.org/10.18653/v1/d18-1188","mag":"2886983157"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1188","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1188","pdf_url":"https://www.aclweb.org/anthology/D18-1188.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1188.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060364305","display_name":"Daya Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Daya Guo","raw_affiliation_strings":["The School of Data and Computer Science, Sun Yat-sen University. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China"],"affiliations":[{"raw_affiliation_string":"The School of Data and Computer Science, Sun Yat-sen University. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034137422","display_name":"Yibo Sun","orcid":"https://orcid.org/0000-0002-9519-2185"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yibo Sun","raw_affiliation_strings":["Harbin Institute of Technology"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110628758","display_name":"Duyu Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Duyu Tang","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042018181","display_name":"Nan Duan","orcid":"https://orcid.org/0000-0002-3387-4674"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nan Duan","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017205177","display_name":"Jian Yin","orcid":"https://orcid.org/0000-0002-1214-5384"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yin","raw_affiliation_strings":["The School of Data and Computer Science, Sun Yat-sen University. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China"],"affiliations":[{"raw_affiliation_string":"The School of Data and Computer Science, Sun Yat-sen University. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012078602","display_name":"Hong Chi","orcid":"https://orcid.org/0000-0001-5049-4633"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hong Chi","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077426201","display_name":"James Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"James Cao","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100704746","display_name":"Peng Chen","orcid":"https://orcid.org/0000-0002-5075-0470"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Peng Chen","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100701572","display_name":"Ming Zhou","orcid":"https://orcid.org/0000-0002-2551-2964"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ming Zhou","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5060364305"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":6.092,"has_fulltext":true,"cited_by_count":61,"citation_normalized_percentile":{"value":0.97018926,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1597","last_page":"1607"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8784817457199097},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.8526343107223511},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7313652038574219},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6454896926879883},{"id":"https://openalex.org/keywords/sql","display_name":"SQL","score":0.4424261748790741},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4421820044517517},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4348571300506592},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37697675824165344},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11847090721130371}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8784817457199097},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.8526343107223511},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7313652038574219},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6454896926879883},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.4424261748790741},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4421820044517517},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4348571300506592},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37697675824165344},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11847090721130371}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1188","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1188","pdf_url":"https://www.aclweb.org/anthology/D18-1188.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d18-1188","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1188","pdf_url":"https://www.aclweb.org/anthology/D18-1188.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7300000190734863}],"awards":[{"id":"https://openalex.org/G1941139581","display_name":null,"funder_award_id":"U1611264","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2428490243","display_name":null,"funder_award_id":"U1711262","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3795889338","display_name":null,"funder_award_id":"U1711","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4044104431","display_name":null,"funder_award_id":"U1401256","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4420049336","display_name":null,"funder_award_id":"U1401256, U1501252","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5229126207","display_name":null,"funder_award_id":"61472453","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7097703722","display_name":null,"funder_award_id":"U171126","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7660082681","display_name":null,"funder_award_id":"U1711261","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8698608195","display_name":null,"funder_award_id":"U1501252","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2886983157.pdf","grobid_xml":"https://content.openalex.org/works/W2886983157.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W1496189301","https://openalex.org/W1902237438","https://openalex.org/W1959608418","https://openalex.org/W2101105183","https://openalex.org/W2101964891","https://openalex.org/W2111742432","https://openalex.org/W2121465811","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2157331557","https://openalex.org/W2161002933","https://openalex.org/W2163274265","https://openalex.org/W2186283982","https://openalex.org/W2250539671","https://openalex.org/W2251044566","https://openalex.org/W2251957808","https://openalex.org/W2252136820","https://openalex.org/W2275056699","https://openalex.org/W2507756961","https://openalex.org/W2511108736","https://openalex.org/W2590657697","https://openalex.org/W2593696076","https://openalex.org/W2619206542","https://openalex.org/W2624022918","https://openalex.org/W2751448157","https://openalex.org/W2757361303","https://openalex.org/W2757978590","https://openalex.org/W2768409085","https://openalex.org/W2949800357","https://openalex.org/W2950037544","https://openalex.org/W2962717047","https://openalex.org/W2962843773","https://openalex.org/W2962874939","https://openalex.org/W2963207291","https://openalex.org/W2963223306","https://openalex.org/W2963319870","https://openalex.org/W2963351776","https://openalex.org/W2963655793","https://openalex.org/W2963675284","https://openalex.org/W2963790827","https://openalex.org/W2963794306","https://openalex.org/W2963822436","https://openalex.org/W2963899988","https://openalex.org/W2963938442","https://openalex.org/W2964053384","https://openalex.org/W2964116568","https://openalex.org/W2964165364","https://openalex.org/W2964224049","https://openalex.org/W2964236999","https://openalex.org/W2964271186","https://openalex.org/W2964308564","https://openalex.org/W3022187094","https://openalex.org/W3101023724","https://openalex.org/W4236265809","https://openalex.org/W4295017198"],"related_works":["https://openalex.org/W579810227","https://openalex.org/W2952780262","https://openalex.org/W2979495269","https://openalex.org/W2392917763","https://openalex.org/W2083429127","https://openalex.org/W2358855848","https://openalex.org/W6643695","https://openalex.org/W4381248170","https://openalex.org/W2817971408","https://openalex.org/W3189621521"],"abstract_inverted_index":{"We":[0,16],"study":[1,19],"how":[2],"to":[3,28,43,67],"learn":[4,44],"a":[5,45,85,92],"semantic":[6,25,50,93],"parser":[7,51,94],"of":[8,55,91,98],"state-of-the-art":[9,76],"accuracy":[10,90],"with":[11,52],"less":[12],"supervised":[13,57,70],"training":[14,58,71,99],"data.":[15,59,100],"conduct":[17],"our":[18],"on":[20],"WikiSQL,":[21],"the":[22,56,68,75,89,96],"largest":[23],"hand-annotated":[24],"parsing":[26],"dataset":[27],"date.":[29],"First,":[30],"we":[31,61,80],"demonstrate":[32],"that":[33,40,63,82],"question":[34,65],"generation":[35,66],"is":[36,84],"an":[37],"effective":[38],"method":[39],"empowers":[41],"us":[42],"state-ofthe-art":[46],"neural":[47],"network":[48],"based":[49],"thirty":[53],"percent":[54],"Second,":[60],"show":[62],"applying":[64],"full":[69],"data":[72],"further":[73],"improves":[74],"model.":[77],"In":[78],"addition,":[79],"observe":[81],"there":[83],"logarithmic":[86],"relationship":[87],"between":[88],"and":[95],"amount":[97]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":12}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}