{"id":"https://openalex.org/W2889587596","doi":"https://doi.org/10.18653/v1/d18-1422","title":"Operation-guided Neural Networks for High Fidelity Data-To-Text Generation","display_name":"Operation-guided Neural Networks for High Fidelity Data-To-Text Generation","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2889587596","doi":"https://doi.org/10.18653/v1/d18-1422","mag":"2889587596"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1422","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1422","pdf_url":"https://www.aclweb.org/anthology/D18-1422.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/D18-1422.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112448786","display_name":"Feng Nie","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Feng Nie","raw_affiliation_strings":["Sun Yat-Sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100376310","display_name":"Jinpeng Wang","orcid":"https://orcid.org/0000-0001-6127-9146"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinpeng Wang","raw_affiliation_strings":["Microsoft Research Asia"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054455207","display_name":"Jin-Ge Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin-Ge Yao","raw_affiliation_strings":["Microsoft Research Asia"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075012459","display_name":"Rong Pan","orcid":"https://orcid.org/0000-0001-5171-8248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rong Pan","raw_affiliation_strings":["Sun Yat-Sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090151187","display_name":"Chin-Yew Lin","orcid":"https://orcid.org/0000-0002-0798-6365"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chin-Yew Lin","raw_affiliation_strings":["Microsoft Research Asia"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5112448786"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.2126,"has_fulltext":true,"cited_by_count":49,"citation_normalized_percentile":{"value":0.96386479,"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":"3879","last_page":"3889"},"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":0.9919999837875366,"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":0.8345890641212463},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.6252410411834717},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6221297979354858},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5967888832092285},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5809692740440369},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.5274874567985535},{"id":"https://openalex.org/keywords/text-generation","display_name":"Text generation","score":0.5253310203552246},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.5066900849342346},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.48066532611846924},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46955612301826477},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.45549851655960083},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17633768916130066},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.10399788618087769}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8345890641212463},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.6252410411834717},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6221297979354858},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5967888832092285},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5809692740440369},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.5274874567985535},{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.5253310203552246},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.5066900849342346},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.48066532611846924},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46955612301826477},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.45549851655960083},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17633768916130066},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.10399788618087769},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1422","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1422","pdf_url":"https://www.aclweb.org/anthology/D18-1422.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/d18-1422","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1422","pdf_url":"https://www.aclweb.org/anthology/D18-1422.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":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321160","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2889587596.pdf","grobid_xml":"https://content.openalex.org/works/W2889587596.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1521413921","https://openalex.org/W1533861849","https://openalex.org/W1732222442","https://openalex.org/W1902237438","https://openalex.org/W1985610876","https://openalex.org/W2058043539","https://openalex.org/W2095652037","https://openalex.org/W2097828466","https://openalex.org/W2107288097","https://openalex.org/W2116716943","https://openalex.org/W2119874156","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2140676672","https://openalex.org/W2141608913","https://openalex.org/W2153854924","https://openalex.org/W2154764394","https://openalex.org/W2157331557","https://openalex.org/W2164079290","https://openalex.org/W2546950329","https://openalex.org/W2561142624","https://openalex.org/W2561658355","https://openalex.org/W2567305082","https://openalex.org/W2589218480","https://openalex.org/W2604799547","https://openalex.org/W2606974598","https://openalex.org/W2613898922","https://openalex.org/W2739046565","https://openalex.org/W2741690481","https://openalex.org/W2891963134","https://openalex.org/W2949626814","https://openalex.org/W2962800603","https://openalex.org/W2962905474","https://openalex.org/W2963091658","https://openalex.org/W2963290255","https://openalex.org/W2963929497","https://openalex.org/W2964165364","https://openalex.org/W2964199361","https://openalex.org/W2964308564","https://openalex.org/W4298324270"],"related_works":["https://openalex.org/W4313443006","https://openalex.org/W2945374968","https://openalex.org/W4385452045","https://openalex.org/W4293777179","https://openalex.org/W2164070813","https://openalex.org/W2135608140","https://openalex.org/W2895525995","https://openalex.org/W4224231624","https://openalex.org/W2332512904","https://openalex.org/W2319626700"],"abstract_inverted_index":{"Recent":[0],"neural":[1,65],"models":[2],"for":[3,95],"data-to-text":[4,66],"generation":[5,67],"are":[6,21,31],"mostly":[7,22],"based":[8],"on":[9,105],"data-driven":[10],"end-toend":[11],"training":[12],"over":[13,52],"encoder-decoder":[14],"networks.":[15],"Even":[16],"though":[17],"the":[18,35,62,115,118,122],"generated":[19,119],"texts":[20,120],"fluent":[23],"and":[24],"informative,":[25],"they":[26],"often":[27],"generate":[28],"descriptions":[29],"that":[30,47],"not":[32],"consistent":[33],"with":[34,83],"input":[36,123],"structured":[37,124],"data.":[38,54,125],"This":[39],"is":[40],"a":[41,75,84,92],"critical":[42],"issue":[43],"especially":[44],"in":[45],"domains":[46],"require":[48],"inference":[49],"or":[50],"calculations":[51],"raw":[53],"In":[55],"this":[56],"paper,":[57],"we":[58],"attempt":[59],"to":[60,98,121],"improve":[61],"fidelity":[63,116],"of":[64,117],"by":[68],"utilizing":[69],"pre-executed":[70,102],"symbolic":[71],"operations.":[72,103],"We":[73],"propose":[74],"framework":[76],"called":[77],"Operationguided":[78],"Attention-based":[79],"sequence-to-sequence":[80],"network":[81],"(OpAtt),":[82],"specifically":[85],"designed":[86],"gating":[87],"mechanism":[88],"as":[89,91],"well":[90],"quantization":[93],"module":[94],"operation":[96],"results":[97],"utilize":[99],"information":[100],"from":[101],"Experiments":[104],"two":[106],"sports":[107],"datasets":[108],"show":[109],"our":[110],"proposed":[111],"method":[112],"clearly":[113],"improves":[114]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":12}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}
