{"id":"https://openalex.org/W2951846787","doi":"https://doi.org/10.18653/v1/p19-1223","title":"E3: Entailment-driven Extracting and Editing for Conversational Machine Reading","display_name":"E3: Entailment-driven Extracting and Editing for Conversational Machine Reading","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2951846787","doi":"https://doi.org/10.18653/v1/p19-1223","mag":"2951846787"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1223","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1223","pdf_url":"https://www.aclweb.org/anthology/P19-1223.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 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P19-1223.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077994189","display_name":"Victor W. Zhong","orcid":"https://orcid.org/0000-0001-9208-4683"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Victor Zhong","raw_affiliation_strings":["University of Washington"],"affiliations":[{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067919401","display_name":"Luke Zettlemoyer","orcid":"https://orcid.org/0009-0008-8296-0764"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luke Zettlemoyer","raw_affiliation_strings":["University of Washington"],"affiliations":[{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5077994189"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":2.6011,"has_fulltext":true,"cited_by_count":25,"citation_normalized_percentile":{"value":0.92103912,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2310","last_page":"2320"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9993000030517578,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9961000084877014,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8467775583267212},{"id":"https://openalex.org/keywords/textual-entailment","display_name":"Textual entailment","score":0.6992483735084534},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6382825374603271},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.6025177836418152},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5775863528251648},{"id":"https://openalex.org/keywords/logical-consequence","display_name":"Logical consequence","score":0.5511927604675293},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3738695979118347},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.12643995881080627}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8467775583267212},{"id":"https://openalex.org/C95318506","wikidata":"https://www.wikidata.org/wiki/Q6588467","display_name":"Textual entailment","level":3,"score":0.6992483735084534},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6382825374603271},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.6025177836418152},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5775863528251648},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.5511927604675293},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3738695979118347},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.12643995881080627},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p19-1223","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1223","pdf_url":"https://www.aclweb.org/anthology/P19-1223.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 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-1223","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1223","pdf_url":"https://www.aclweb.org/anthology/P19-1223.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 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G3366966419","display_name":null,"funder_award_id":"W911NF-16-1","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G6497436218","display_name":"CAREER: Learning Scalable Models for Grounded Semantic Parsing","funder_award_id":"1252835","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G657448715","display_name":null,"funder_award_id":"W911NF-16-1-","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7750423253","display_name":null,"funder_award_id":"IIS-1562364","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8533175095","display_name":"RI: Medium: Broad-Coverage Semantic Parsing: Linguistic Representation Learning from Crowd-Scale Data","funder_award_id":"1562364","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8998121839","display_name":null,"funder_award_id":"911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2951846787.pdf","grobid_xml":"https://content.openalex.org/works/W2951846787.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1852412531","https://openalex.org/W1948566616","https://openalex.org/W1975244201","https://openalex.org/W1987001238","https://openalex.org/W2064675550","https://openalex.org/W2095705004","https://openalex.org/W2107598941","https://openalex.org/W2112085250","https://openalex.org/W2123442489","https://openalex.org/W2127795553","https://openalex.org/W2133564696","https://openalex.org/W2250539671","https://openalex.org/W2251058040","https://openalex.org/W2251235149","https://openalex.org/W2251913848","https://openalex.org/W2413794162","https://openalex.org/W2525778437","https://openalex.org/W2728059831","https://openalex.org/W2804010326","https://openalex.org/W2888302696","https://openalex.org/W2889344053","https://openalex.org/W2891304738","https://openalex.org/W2894293047","https://openalex.org/W2896457183","https://openalex.org/W2963064439","https://openalex.org/W2963233086","https://openalex.org/W2963341956","https://openalex.org/W2963347649","https://openalex.org/W2963403868","https://openalex.org/W2963748441","https://openalex.org/W2963797754","https://openalex.org/W2964101860","https://openalex.org/W2964116313","https://openalex.org/W2964121744","https://openalex.org/W2964223283","https://openalex.org/W2964308564","https://openalex.org/W4289489602","https://openalex.org/W4300756893","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2118335617","https://openalex.org/W12963412","https://openalex.org/W2169644218","https://openalex.org/W2947884672","https://openalex.org/W2053800966","https://openalex.org/W2937401546","https://openalex.org/W2524172264","https://openalex.org/W574870997","https://openalex.org/W128392744","https://openalex.org/W1531113454"],"abstract_inverted_index":{"Conversational":[0],"machine":[1,82,126],"reading":[2,83,127],"systems":[3,144],"help":[4],"users":[5],"answer":[6],"high-level":[7],"questions":[8,116],"(e.g.":[9,32,59],"determine":[10],"if":[11],"they":[12,19,34],"qualify":[13],"for":[14,117,178],"particular":[15],"government":[16,62],"benefits)":[17],"when":[18],"do":[20],"not":[21],"know":[22],"the":[23,28,53,65,75,94,104,118,121],"exact":[24],"rules":[25,48,92],"by":[26,103,154],"which":[27,64,100,108,157],"determination":[29],"is":[30,45],"made":[31],"whether":[33],"need":[35,110],"certain":[36],"income":[37],"levels":[38],"or":[39],"veteran":[40],"status).":[41],"The":[42],"key":[43],"challenge":[44],"that":[46,85],"these":[47],"are":[49,101],"only":[50],"provided":[51],"in":[52],"form":[54],"of":[55,90],"a":[56,79,88,139,148,167],"procedural":[57,95],"text":[58,96],"guidelines":[60],"from":[61,93],"website)":[63],"system":[66],"must":[67],"read":[68],"to":[69,73,111,114,161,171],"figure":[70],"out":[71],"what":[72],"ask":[74],"user.":[76,119],"We":[77,174],"present":[78],"new":[80,140,149],"conversational":[81,105,125],"model":[84],"jointly":[86],"extracts":[87],"set":[89],"decision":[91],"while":[97],"reasoning":[98],"about":[99],"entailed":[102],"history":[106],"and":[107,132,181],"still":[109,159],"be":[112,162],"edited":[113],"create":[115],"On":[120],"recently":[122],"introduced":[123],"ShARC":[124],"dataset,":[128],"our":[129,179],"Entailment-driven":[130],"Extract":[131],"Edit":[133],"network":[134],"(E":[135],"3":[136,165],")":[137],"achieves":[138],"state-of-theart,":[141],"outperforming":[142],"existing":[143],"as":[145,147],"well":[146],"BERT-based":[150],"baseline.":[151],"In":[152],"addition,":[153],"explicitly":[155],"highlighting":[156],"information":[158],"needs":[160],"gathered,":[163],"E":[164],"provides":[166],"more":[168],"explainable":[169],"alternative":[170],"prior":[172],"work.":[173],"release":[175],"source":[176],"code":[177],"models":[180],"experiments":[182],"at":[183],"https://github.com/vzhong/e3.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":3}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
