{"id":"https://openalex.org/W4290927728","doi":"https://doi.org/10.1145/3534678.3539082","title":"A Logic Aware Neural Generation Method for Explainable Data-to-text","display_name":"A Logic Aware Neural Generation Method for Explainable Data-to-text","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290927728","doi":"https://doi.org/10.1145/3534678.3539082"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539082","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539082","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040605807","display_name":"Xiexiong Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiexiong Lin","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003503307","display_name":"Huaisong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huaisong Li","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100767738","display_name":"Tao Huang","orcid":"https://orcid.org/0000-0002-8098-8906"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao Huang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100431127","display_name":"Feng Wang","orcid":"https://orcid.org/0000-0001-6975-4335"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng Wang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016533099","display_name":"Linlin Chao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Linlin Chao","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041921496","display_name":"Fuzhen Zhuang","orcid":"https://orcid.org/0000-0001-9170-7009"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuzhen Zhuang","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074676968","display_name":"Taifeng Wang","orcid":"https://orcid.org/0009-0007-1116-0228"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taifeng Wang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100437465","display_name":"Tianyi Zhang","orcid":"https://orcid.org/0009-0001-8703-0100"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tianyi Zhang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3374,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54066588,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3318","last_page":"3326"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.998199999332428,"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":0.998199999332428,"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":0.9970999956130981,"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"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9663000106811523,"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":0.8018967509269714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5767536163330078},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5639875531196594},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.46598806977272034},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.45885470509529114},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45834729075431824},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42048782110214233},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.4160313904285431},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.4123265743255615},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3617553114891052}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8018967509269714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5767536163330078},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5639875531196594},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.46598806977272034},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.45885470509529114},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45834729075431824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42048782110214233},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.4160313904285431},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.4123265743255615},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3617553114891052},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539082","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539082","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5191158251","display_name":null,"funder_award_id":"2021ZD0113602","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1985610876","https://openalex.org/W1992365067","https://openalex.org/W2003170434","https://openalex.org/W2058043539","https://openalex.org/W2116716943","https://openalex.org/W2164079290","https://openalex.org/W2745461083","https://openalex.org/W2752442988","https://openalex.org/W2786983967","https://openalex.org/W2803299344","https://openalex.org/W2803930360","https://openalex.org/W2981852735","https://openalex.org/W2983537304","https://openalex.org/W2983775903","https://openalex.org/W2998693386","https://openalex.org/W3003774665","https://openalex.org/W3007604991","https://openalex.org/W3011309387","https://openalex.org/W3017340784","https://openalex.org/W4288089799"],"related_works":["https://openalex.org/W2955859849","https://openalex.org/W2152921782","https://openalex.org/W382594479","https://openalex.org/W2470045054","https://openalex.org/W2575772232","https://openalex.org/W2151245229","https://openalex.org/W2140902089","https://openalex.org/W1510553545","https://openalex.org/W3020827637","https://openalex.org/W199086061"],"abstract_inverted_index":{"The":[0,85],"most":[1],"notable":[2],"neural":[3],"data-to-text":[4,59],"approaches":[5],"generate":[6],"natural":[7,44,83],"language":[8,45],"from":[9,74],"structural":[10,19],"data":[11,30,76],"relying":[12],"on":[13,179],"the":[14,18,23,28,32,62,103,122,131,139,147,163,186],"surface":[15],"form":[16],"of":[17,105,166],"content,":[20],"which":[21,96],"ignores":[22],"underlying":[24],"logical":[25,38],"correlation":[26],"between":[27,149],"input":[29,75,151],"and":[31,40,77,108,152,203],"target":[33,153],"text.":[34,154],"Moreover,":[35],"identifying":[36],"such":[37],"associations":[39],"explaining":[41,78],"them":[42],"in":[43,82,168,192,200,209],"is":[46,88,97,143,159],"desirable":[47],"but":[48],"not":[49],"yet":[50],"studied.":[51],"In":[52],"this":[53],"paper,":[54],"we":[55,112],"introduce":[56],"a":[57,89,98,118,135,156],"practical":[58],"method":[60,87,188],"for":[61,66],"logic-critical":[63],"scenario,":[64],"specifically":[65],"anti-money":[67,180],"laundering":[68,181],"applications.":[69],"It":[70],"involves":[71],"detecting":[72],"risks":[73],"any":[79],"abnormal":[80],"behaviors":[81],"language.":[84],"proposed":[86,187],"Logic":[90],"Aware":[91],"Neural":[92],"Generation":[93],"framework":[94],"(LANG),":[95],"preliminary":[99],"attempt":[100],"to":[101,117,129,145,161,172],"explore":[102],"integration":[104],"logic":[106,119,141],"modeling":[107],"text":[109],"generation.":[110],"Concretely,":[111],"first":[113],"convert":[114],"expert":[115,132],"rules":[116],"graph.":[120],"Then,":[121],"model":[123],"utilizes":[124],"meta":[125],"path":[126],"based":[127],"encoder":[128],"exploit":[130],"knowledge.":[133],"Besides,":[134],"retriever":[136],"module":[137],"with":[138,196,206],"encoded":[140],"knowledge":[142],"used":[144],"bridge":[146],"gap":[148],"numeric":[150],"Finally,":[155],"rule-constrained":[157],"loss":[158],"leveraged":[160],"improve":[162],"generation":[164],"probability":[165],"tokens":[167],"rule":[169],"recalled":[170],"statements":[171],"ensure":[173],"accuracy.":[174],"We":[175],"conduct":[176],"extensive":[177],"experiments":[178],"data.":[182],"Results":[183],"show":[184],"that":[185],"significantly":[189],"outperforms":[190],"baselines":[191],"both":[193],"objective":[194],"measures":[195,205],"relative":[197],"35%":[198],"improvements":[199],"F1":[201],"score":[202],"subjective":[204],"30%":[207],"improvement":[208],"human":[210],"preference.":[211]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}