{"id":"https://openalex.org/W3010949594","doi":"https://doi.org/10.1109/ialp48816.2019.9037729","title":"How to Answer Comparison Questions","display_name":"How to Answer Comparison Questions","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3010949594","doi":"https://doi.org/10.1109/ialp48816.2019.9037729","mag":"3010949594"},"language":"en","primary_location":{"id":"doi:10.1109/ialp48816.2019.9037729","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ialp48816.2019.9037729","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Asian Language Processing (IALP)","raw_type":"proceedings-article"},"type":"article","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/A5036917650","display_name":"Hongxuan Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongxuan Tang","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100609882","display_name":"Yu Hong","orcid":"https://orcid.org/0000-0003-0606-3718"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Hong","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100615518","display_name":"Xin Chen","orcid":"https://orcid.org/0009-0003-0995-1152"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Chen","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043119497","display_name":"Kaili Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaili Wu","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100402911","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0002-3895-5510"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.1446,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.61874105,"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":"331","last_page":"336"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.996999979019165,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9907000064849854,"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":0.5656823515892029}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5656823515892029}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ialp48816.2019.9037729","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ialp48816.2019.9037729","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Asian Language Processing (IALP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1522301498","https://openalex.org/W1544827683","https://openalex.org/W1832693441","https://openalex.org/W2125436846","https://openalex.org/W2157331557","https://openalex.org/W2250539671","https://openalex.org/W2507756961","https://openalex.org/W2516930406","https://openalex.org/W2551396370","https://openalex.org/W2889787757","https://openalex.org/W2896457183","https://openalex.org/W2949134692","https://openalex.org/W2949615363","https://openalex.org/W2952230306","https://openalex.org/W2962808855","https://openalex.org/W2962985038","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963748441","https://openalex.org/W2964121744","https://openalex.org/W2964223283","https://openalex.org/W2964267515","https://openalex.org/W4385245566","https://openalex.org/W6607333740","https://openalex.org/W6631190155","https://openalex.org/W6632455782","https://openalex.org/W6678890848","https://openalex.org/W6725207838","https://openalex.org/W6726253136","https://openalex.org/W6729654139","https://openalex.org/W6739901393","https://openalex.org/W6750384732","https://openalex.org/W6754375359","https://openalex.org/W6754964836","https://openalex.org/W6755207826"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"\u201cWhich":[0],"city":[1],"has":[2,46],"the":[3,12,19,23,31,98,113,137,148],"larger":[4],"population,":[5],"Tokyo":[6],"or":[7,119],"New":[8],"York?\u201d.":[9],"To":[10],"answer":[11,32,42,78],"question,":[13],"in":[14,93],"general,":[15],"we":[16,67],"necessarily":[17],"obtain":[18],"prior":[20],"knowledge":[21],"about":[22,151],"populations":[24],"of":[25,59,100,107,112,126,145,163],"both":[26],"cities,":[27],"and":[28,128],"accordingly":[29],"determine":[30],"by":[33],"numeric":[34],"comparison.":[35],"Using":[36],"Machine":[37],"Reading":[38],"Comprehension":[39],"(MRC)":[40],"to":[41,55,77],"such":[43],"a":[44,48,57,69,86,94,161],"question":[45],"become":[47],"popular":[49],"research":[50],"topic,":[51],"which":[52,74,90],"is":[53,75,83],"referred":[54],"as":[56,85,121,123],"task":[58],"Comparison":[60],"Question":[61],"Answering":[62],"(CQA).":[63],"In":[64,155],"this":[65],"paper,":[66],"propose":[68],"novel":[70],"neural":[71,88],"CQA":[72],"model":[73,82],"trained":[76],"comparison":[79],"question.":[80],"The":[81,131],"designed":[84],"sophisticated":[87],"network":[89],"performs":[91,158],"inference":[92],"step-by-step":[95],"pipeline,":[96],"including":[97,166],"steps":[99],"attentive":[101],"entity":[102],"detection":[103],"(e.g.,":[104,110],"\u201ccity\u201d),":[105],"alignment":[106],"comparable":[108],"attributes":[109],"\u201cpopulation\u201d":[111],"target":[114],"\u201ccities\u201d),":[115],"contrast":[116],"calculation":[117],"(larger":[118],"smaller),":[120],"well":[122],"binary":[124],"classification":[125],"positive":[127],"negative":[129],"answers.":[130],"experimentation":[132],"on":[133],"HotpotQA":[134],"illustrates":[135],"that":[136],"proposed":[138],"method":[139],"achieves":[140],"an":[141],"average":[142],"F1":[143,153],"score":[144],"63.09%,":[146],"outperforming":[147],"baseline":[149],"with":[150],"10%":[152],"scores.":[154],"addition,":[156],"it":[157],"better":[159],"than":[160],"series":[162],"competitive":[164],"models,":[165],"DecompRC,":[167],"BERT.":[168]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}