{"id":"https://openalex.org/W2889670144","doi":"https://doi.org/10.18653/v1/d18-1427","title":"Answer-focused and Position-aware Neural Question Generation","display_name":"Answer-focused and Position-aware Neural Question Generation","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2889670144","doi":"https://doi.org/10.18653/v1/d18-1427","mag":"2889670144"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1427","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1427","pdf_url":"https://www.aclweb.org/anthology/D18-1427.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/D18-1427.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039337290","display_name":"Xingwu Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingwu Sun","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375105","display_name":"Jing Liu","orcid":"https://orcid.org/0000-0003-1727-6321"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Liu","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000515409","display_name":"Yajuan Lyu","orcid":null},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajuan Lyu","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101588658","display_name":"Wei He","orcid":"https://orcid.org/0009-0000-8005-062X"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei He","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047724561","display_name":"Yanjun Ma","orcid":"https://orcid.org/0000-0002-1232-6142"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjun Ma","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102024497","display_name":"Shi Wang","orcid":"https://orcid.org/0000-0002-1329-2415"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shi Wang","raw_affiliation_strings":["Institute of Computing Technology Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computing Technology Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":196,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3930","last_page":"3939"},"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":0.9983999729156494,"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":0.9922999739646912,"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":0.7746591567993164},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6854932308197021},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6182225942611694},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5942665934562683},{"id":"https://openalex.org/keywords/interrogative","display_name":"Interrogative","score":0.5764739513397217},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5345956683158875},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5234193801879883},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.52006596326828},{"id":"https://openalex.org/keywords/copying","display_name":"Copying","score":0.5199683904647827},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5168643593788147},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5156830549240112},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.5015013217926025},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.49924302101135254},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.42662903666496277},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3269946873188019},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.32415956258773804},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.196742981672287},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09339231252670288}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7746591567993164},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6854932308197021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6182225942611694},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5942665934562683},{"id":"https://openalex.org/C57098296","wikidata":"https://www.wikidata.org/wiki/Q12021746","display_name":"Interrogative","level":2,"score":0.5764739513397217},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5345956683158875},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5234193801879883},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.52006596326828},{"id":"https://openalex.org/C2779151265","wikidata":"https://www.wikidata.org/wiki/Q1156791","display_name":"Copying","level":2,"score":0.5199683904647827},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5168643593788147},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5156830549240112},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.5015013217926025},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.49924302101135254},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.42662903666496277},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3269946873188019},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.32415956258773804},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.196742981672287},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09339231252670288},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1427","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1427","pdf_url":"https://www.aclweb.org/anthology/D18-1427.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/d18-1427","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1427","pdf_url":"https://www.aclweb.org/anthology/D18-1427.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":[],"awards":[{"id":"https://openalex.org/G3873607609","display_name":null,"funder_award_id":"2014CB340505","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2889670144.pdf","grobid_xml":"https://content.openalex.org/works/W2889670144.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1531374185","https://openalex.org/W2095705004","https://openalex.org/W2101105183","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2146502635","https://openalex.org/W2166348853","https://openalex.org/W2250521169","https://openalex.org/W2250539671","https://openalex.org/W2557480356","https://openalex.org/W2606333299","https://openalex.org/W2606974598","https://openalex.org/W2624022918","https://openalex.org/W2757978590","https://openalex.org/W2759211898","https://openalex.org/W2951534261","https://openalex.org/W2962717047","https://openalex.org/W2962944953","https://openalex.org/W2963558220","https://openalex.org/W2963748441","https://openalex.org/W2964165364","https://openalex.org/W2964236999","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W3084147960","https://openalex.org/W2380118150","https://openalex.org/W2554293154","https://openalex.org/W2382633796","https://openalex.org/W2371032728","https://openalex.org/W4308771405","https://openalex.org/W2386303189","https://openalex.org/W2911655849","https://openalex.org/W4286432911","https://openalex.org/W3134737443"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,37,92,106,109],"focus":[4],"on":[5],"the":[6,17,56,63,73,77,85,111,115,119,122,128,131,147,161,165],"problem":[7],"of":[8,76,127,130,149],"question":[9,33,51,99],"generation":[10,100],"(QG).":[11],"Recent":[12],"neural":[13,98],"networkbased":[14],"approaches":[15],"employ":[16],"sequence-tosequence":[18],"model":[19,61,110,123,158],"which":[20],"takes":[21],"an":[22,94],"answer":[23,57],"and":[24,29,70,82,96,118,163],"its":[25],"context":[26,64,116,132],"as":[27,34],"input":[28],"generates":[30],"a":[31,139],"relevant":[32,83],"output.":[35],"However,":[36],"observe":[38],"two":[39,90],"major":[40],"issues":[41],"with":[42],"these":[43,89],"approaches:":[44],"(1)":[45],"The":[46,60,152],"generated":[47],"interrogative":[48],"words":[49,65,78,117,133],"(or":[50],"words)":[52],"do":[53],"not":[54],"match":[55],"type.":[58],"(2)":[59,103],"copies":[62],"that":[66,79,108,156],"are":[67,80],"far":[68],"from":[69],"irrelevant":[71],"to":[72,84,137,145],"answer,":[74],"instead":[75],"close":[81],"answer.":[86,120],"To":[87],"address":[88],"issues,":[91],"propose":[93],"answer-focused":[95],"position-aware":[97],"model.":[101,151],"(":[102],"By":[104],"position-aware,":[105],"mean":[107],"relative":[112],"distance":[113],"between":[114],"Hence":[121],"can":[124],"be":[125],"aware":[126],"position":[129],"when":[134],"copying":[135],"them":[136],"generate":[138],"question.":[140],"We":[141],"conduct":[142],"extensive":[143],"experiments":[144],"examine":[146],"effectiveness":[148],"our":[150,157],"experimental":[153],"results":[154],"show":[155],"significantly":[159],"improves":[160],"baseline":[162],"outperforms":[164],"state-of-the-art":[166],"system.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":27},{"year":2021,"cited_by_count":42},{"year":2020,"cited_by_count":44},{"year":2019,"cited_by_count":37},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}