{"id":"https://openalex.org/W2892043231","doi":"https://doi.org/10.18653/v1/d18-1266","title":"Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations","display_name":"Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2892043231","doi":"https://doi.org/10.18653/v1/d18-1266","mag":"2892043231"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1266","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1266","pdf_url":"https://www.aclweb.org/anthology/D18-1266.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":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1266.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088218114","display_name":"Dipendra Misra","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I48018076","display_name":"Christ University","ror":"https://ror.org/022tv9y30","country_code":"IN","type":"education","lineage":["https://openalex.org/I48018076"]}],"countries":["IN","US"],"is_corresponding":false,"raw_author_name":"Dipendra Misra","raw_affiliation_strings":["? Cornell University,","Charlotte Barras England","Christelle Le Duff France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"? Cornell University,","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Charlotte Barras England","institution_ids":[]},{"raw_affiliation_string":"Christelle Le Duff France","institution_ids":["https://openalex.org/I48018076"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076904467","display_name":"Ming\u2010Wei Chang","orcid":"https://orcid.org/0000-0002-0137-8895"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ming-Wei Chang","raw_affiliation_strings":["Google AI Language,  JD AI Research","Google AI Language, \u21e7 JD AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google AI Language,  JD AI Research","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google AI Language, \u21e7 JD AI Research","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101727205","display_name":"Xiaodong He","orcid":"https://orcid.org/0000-0002-9463-9168"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaodong He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066873932","display_name":"Wen-tau Yih","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156221","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210156221"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wen-tau Yih","raw_affiliation_strings":["Allen Institute for Artificial Intelligence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Allen Institute for Artificial Intelligence","institution_ids":["https://openalex.org/I4210156221"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5076904467"],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":25,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2442","last_page":"2452"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9918000102043152,"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/parsing","display_name":"Parsing","score":0.8275952339172363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7739418745040894},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.666221559047699},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5734859704971313},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.46511295437812805},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4283851981163025},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.06897568702697754}],"concepts":[{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.8275952339172363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7739418745040894},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.666221559047699},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5734859704971313},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.46511295437812805},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4283851981163025},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.06897568702697754}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1266","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1266","pdf_url":"https://www.aclweb.org/anthology/D18-1266.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/d18-1266","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1266","pdf_url":"https://www.aclweb.org/anthology/D18-1266.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":[{"display_name":"Quality Education","score":0.550000011920929,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320308943","display_name":"Microsoft Research","ror":"https://ror.org/00d0nc645"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2892043231.pdf","grobid_xml":"https://content.openalex.org/works/W2892043231.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W91928571","https://openalex.org/W122926584","https://openalex.org/W147273232","https://openalex.org/W162171320","https://openalex.org/W1496189301","https://openalex.org/W1510718138","https://openalex.org/W1559723967","https://openalex.org/W1934909785","https://openalex.org/W2049633694","https://openalex.org/W2098441518","https://openalex.org/W2101534792","https://openalex.org/W2105644991","https://openalex.org/W2111742432","https://openalex.org/W2116410915","https://openalex.org/W2119807359","https://openalex.org/W2147196093","https://openalex.org/W2161002933","https://openalex.org/W2161877964","https://openalex.org/W2189089430","https://openalex.org/W2250808860","https://openalex.org/W2251648989","https://openalex.org/W2252136820","https://openalex.org/W2310425190","https://openalex.org/W2541794668","https://openalex.org/W2564080101","https://openalex.org/W2612228435","https://openalex.org/W2751448157","https://openalex.org/W2757361303","https://openalex.org/W2953182116","https://openalex.org/W2962977959","https://openalex.org/W2963167310","https://openalex.org/W2963167649","https://openalex.org/W2963367210","https://openalex.org/W2963655793","https://openalex.org/W2964224049","https://openalex.org/W4230563027"],"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/W6643695","https://openalex.org/W4381248170","https://openalex.org/W3189621521","https://openalex.org/W2173794830","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Semantic":[0],"parsing":[1],"from":[2],"denotations":[3,15],"faces":[4],"two":[5],"key":[6],"challenges":[7],"in":[8],"model":[9,29,85,104],"training:":[10],"(1)":[11],"given":[12],"only":[13],"the":[14,27,47,58],"(e.g.,":[16],"answers),":[17],"search":[18,48],"for":[19,65],"good":[20],"candidate":[21],"semantic":[22,51],"parses,":[23],"and":[24,35],"(2)":[25],"choose":[26],"best":[28],"update":[30,72],"algorithm.":[31],"We":[32],"propose":[33,70],"effective":[34],"general":[36],"solutions":[37],"to":[38,57,100],"each":[39],"of":[40,79],"them.":[41],"Using":[42],"policy":[43],"shaping,":[44],"we":[45,69],"bias":[46],"procedure":[49],"towards":[50],"parses":[52],"that":[53,74,105],"are":[54],"more":[55],"compatible":[56],"text,":[59],"which":[60,82],"provide":[61],"better":[62],"supervision":[63],"signals":[64],"training.":[66],"In":[67],"addition,":[68],"an":[71],"equation":[73],"generalizes":[75],"three":[76],"different":[77],"families":[78],"learning":[80],"algorithms,":[81],"enables":[83],"fast":[84],"exploration.":[86],"When":[87],"experimented":[88],"on":[89,112],"a":[90,101],"recently":[91],"proposed":[92],"sequential":[93],"question":[94],"answering":[95],"dataset,":[96],"our":[97],"framework":[98],"leads":[99],"new":[102],"state-of-theart":[103],"outperforms":[106],"previous":[107],"work":[108],"by":[109],"5.0%":[110],"absolute":[111],"exact":[113],"match":[114],"accuracy.":[115]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
