{"id":"https://openalex.org/W4284674228","doi":"https://doi.org/10.1145/3477495.3531815","title":"Conversational Question Answering on Heterogeneous Sources","display_name":"Conversational Question Answering on Heterogeneous Sources","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4284674228","doi":"https://doi.org/10.1145/3477495.3531815"},"language":"en","primary_location":{"id":"doi:10.1145/3477495.3531815","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477495.3531815","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3477495.3531815","source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3477495.3531815","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039326753","display_name":"Philipp Christmann","orcid":"https://orcid.org/0000-0003-0857-7245"},"institutions":[{"id":"https://openalex.org/I4210109712","display_name":"Max Planck Institute for Informatics","ror":"https://ror.org/01w19ak89","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210109712"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Philipp Christmann","raw_affiliation_strings":["Max Planck Institute for Informatics, Saarbr\u00fccken, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Max Planck Institute for Informatics, Saarbr\u00fccken, Germany","institution_ids":["https://openalex.org/I4210109712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042165238","display_name":"Rishiraj Saha Roy","orcid":"https://orcid.org/0000-0002-5774-5658"},"institutions":[{"id":"https://openalex.org/I4210109712","display_name":"Max Planck Institute for Informatics","ror":"https://ror.org/01w19ak89","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210109712"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Rishiraj Saha Roy","raw_affiliation_strings":["Max Planck Institute for Informatics, Saarbr\u00fccken, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Max Planck Institute for Informatics, Saarbr\u00fccken, Germany","institution_ids":["https://openalex.org/I4210109712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088135366","display_name":"Gerhard Weikum","orcid":"https://orcid.org/0000-0003-4959-6098"},"institutions":[{"id":"https://openalex.org/I4210109712","display_name":"Max Planck Institute for Informatics","ror":"https://ror.org/01w19ak89","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210109712"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gerhard Weikum","raw_affiliation_strings":["Max Planck Institute for Informatics, Saarbr\u00fccken, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Max Planck Institute for Informatics, Saarbr\u00fccken, Germany","institution_ids":["https://openalex.org/I4210109712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210109712"],"apc_list":null,"apc_paid":null,"fwci":4.0486,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.95118935,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"144","last_page":"154"},"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":0.9994999766349792,"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":0.9950000047683716,"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":0.834824800491333},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.7030352354049683},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6652676463127136},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6169275045394897},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5640325546264648},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.5608418583869934},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5406332612037659},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.48906832933425903},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46796852350234985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4522458016872406},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4492715895175934},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4464249610900879},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4257693886756897},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3788771331310272},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.13677045702934265}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.834824800491333},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7030352354049683},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6652676463127136},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6169275045394897},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5640325546264648},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.5608418583869934},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5406332612037659},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.48906832933425903},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46796852350234985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4522458016872406},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4492715895175934},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4464249610900879},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4257693886756897},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3788771331310272},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.13677045702934265},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477495.3531815","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477495.3531815","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3477495.3531815","source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3477495.3531815","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3477495.3531815","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3477495.3531815","source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6000000238418579,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4284674228.pdf","grobid_xml":"https://content.openalex.org/works/W4284674228.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W102708294","https://openalex.org/W2022166150","https://openalex.org/W2341824259","https://openalex.org/W2612228435","https://openalex.org/W2742113702","https://openalex.org/W2786472750","https://openalex.org/W2888302696","https://openalex.org/W2949694638","https://openalex.org/W2962985038","https://openalex.org/W2964212344","https://openalex.org/W2970723181","https://openalex.org/W2970916293","https://openalex.org/W2970996870","https://openalex.org/W2998536339","https://openalex.org/W3027639267","https://openalex.org/W3100907046","https://openalex.org/W3101082165","https://openalex.org/W3115037692","https://openalex.org/W3154224467","https://openalex.org/W3154271556","https://openalex.org/W3156789018","https://openalex.org/W3163109966","https://openalex.org/W3165875610","https://openalex.org/W3175542978","https://openalex.org/W3211142893","https://openalex.org/W4221159960","https://openalex.org/W4226163730","https://openalex.org/W4302945789","https://openalex.org/W6600002382"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2384605597","https://openalex.org/W4231274751","https://openalex.org/W3134247745","https://openalex.org/W4226243593","https://openalex.org/W3172691639","https://openalex.org/W2963582704","https://openalex.org/W4285816982"],"abstract_inverted_index":{"Conversational":[0],"question":[1,84,139,142],"answering":[2],"(ConvQA)":[3],"tackles":[4],"sequential":[5],"information":[6],"needs":[7],"where":[8],"contexts":[9],"in":[10,72],"follow-up":[11],"questions":[12],"are":[13],"left":[14],"implicit.":[15],"Current":[16],"ConvQA":[17,67,123],"systems":[18],"operate":[19],"over":[20,68,124],"homogeneous":[21],"sources":[22],"of":[23,37,45,50,81,150],"information:":[24],"either":[25],"a":[26,31,35,107],"knowledge":[27],"base":[28],"(KB),":[29],"or":[30,34],"text":[32],"corpus,":[33],"collection":[36],"tables.":[38],"This":[39],"paper":[40],"addresses":[41],"the":[42,112,118,146],"novel":[43],"issue":[44],"jointly":[46],"tapping":[47],"into":[48],"all":[49],"these":[51],"together,":[52],"this":[53,91],"way":[54],"boosting":[55],"answer":[56],"coverage":[57],"and":[58,85,102,104,116,141,148],"confidence.":[59],"We":[60,114],"present":[61],"CONVINSE,":[62],"an":[63,77,82],"end-to-end":[64],"pipeline":[65],"for":[66,122],"heterogeneous":[69,125],"sources,":[70,126],"operating":[71],"three":[73],"stages:":[74],"i)":[75],"learning":[76],"explicit":[78],"structured":[79],"representation":[80,93],"incoming":[83],"its":[86],"conversational":[87],"context,":[88],"ii)":[89],"harnessing":[90],"frame-like":[92],"to":[94,110,154],"uniformly":[95],"capture":[96],"relevant":[97],"evidences":[98],"from":[99],"KB,":[100],"text,":[101],"tables,":[103],"iii)":[105],"running":[106],"fusion-in-decoder":[108],"model":[109],"generate":[111],"answer.":[113],"construct":[115],"release":[117],"first":[119],"benchmark,":[120],"ConvMix,":[121],"comprising":[127],"3000":[128],"real-user":[129],"conversations":[130],"with":[131,135],"16000":[132],"questions,":[133],"along":[134],"entity":[136],"annotations,":[137],"completed":[138],"utterances,":[140],"paraphrases.":[143],"Experiments":[144],"demonstrate":[145],"viability":[147],"advantages":[149],"our":[151],"method,":[152],"compared":[153],"state-of-the-art":[155],"baselines.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
