{"id":"https://openalex.org/W4312561067","doi":"https://doi.org/10.1109/taffc.2022.3212994","title":"Towards Contrastive Context-Aware Conversational Emotion Recognition","display_name":"Towards Contrastive Context-Aware Conversational Emotion Recognition","publication_year":2022,"publication_date":"2022-10-01","ids":{"openalex":"https://openalex.org/W4312561067","doi":"https://doi.org/10.1109/taffc.2022.3212994"},"language":"en","primary_location":{"id":"doi:10.1109/taffc.2022.3212994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2022.3212994","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-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/A5101776258","display_name":"Hanqing Zhang","orcid":"https://orcid.org/0000-0002-8715-0532"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hanqing Zhang","raw_affiliation_strings":["School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050532724","display_name":"Dawei Song","orcid":"https://orcid.org/0000-0002-8660-3608"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Song","raw_affiliation_strings":["School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101776258"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":1.7901,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.87458789,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"13","issue":"4","first_page":"1879","last_page":"1891"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","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/T10664","display_name":"Sentiment Analysis and Opinion Mining","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/T10667","display_name":"Emotion and Mood Recognition","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9940000176429749,"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/utterance","display_name":"Utterance","score":0.8254371285438538},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7864985466003418},{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.6908013224601746},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.653398871421814},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6166441440582275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5343317985534668},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.5022284984588623},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.48942509293556213},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4422772526741028},{"id":"https://openalex.org/keywords/semantic-interpretation","display_name":"Semantic interpretation","score":0.4169784188270569},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.21567729115486145},{"id":"https://openalex.org/keywords/communication","display_name":"Communication","score":0.1493287980556488},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.0772370994091034}],"concepts":[{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.8254371285438538},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7864985466003418},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.6908013224601746},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.653398871421814},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6166441440582275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5343317985534668},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.5022284984588623},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.48942509293556213},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4422772526741028},{"id":"https://openalex.org/C193125573","wikidata":"https://www.wikidata.org/wiki/Q7449065","display_name":"Semantic interpretation","level":2,"score":0.4169784188270569},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.21567729115486145},{"id":"https://openalex.org/C46312422","wikidata":"https://www.wikidata.org/wiki/Q11024","display_name":"Communication","level":1,"score":0.1493287980556488},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0772370994091034},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taffc.2022.3212994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2022.3212994","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G4069956361","display_name":null,"funder_award_id":"TC20201228005","funder_id":"https://openalex.org/F4320322183","funder_display_name":"Huawei Technologies"}],"funders":[{"id":"https://openalex.org/F4320322183","display_name":"Huawei Technologies","ror":"https://ror.org/00cmhce21"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W580074167","https://openalex.org/W2138204974","https://openalex.org/W2138621090","https://openalex.org/W2141986476","https://openalex.org/W2146334809","https://openalex.org/W2740550900","https://openalex.org/W2786464815","https://openalex.org/W2842511635","https://openalex.org/W2952592807","https://openalex.org/W2962796276","https://openalex.org/W2963873807","https://openalex.org/W2964300796","https://openalex.org/W2965373594","https://openalex.org/W2965453734","https://openalex.org/W2970431814","https://openalex.org/W2996849360","https://openalex.org/W2997793532","https://openalex.org/W3005680577","https://openalex.org/W3035524453","https://openalex.org/W3038471032","https://openalex.org/W3048392339","https://openalex.org/W3090813284","https://openalex.org/W3098556456","https://openalex.org/W3100110884","https://openalex.org/W3100955355","https://openalex.org/W3103338449","https://openalex.org/W3110738190","https://openalex.org/W3113800548","https://openalex.org/W3118668786","https://openalex.org/W3120680448","https://openalex.org/W3128699722","https://openalex.org/W3131157458","https://openalex.org/W3175552668","https://openalex.org/W3176399185","https://openalex.org/W3176849538","https://openalex.org/W3200681609","https://openalex.org/W3202775307","https://openalex.org/W3208538591","https://openalex.org/W3216650611","https://openalex.org/W4297808394","https://openalex.org/W6617055948","https://openalex.org/W6680106237","https://openalex.org/W6681249710","https://openalex.org/W6748176785","https://openalex.org/W6766673545","https://openalex.org/W6774314701","https://openalex.org/W6779872132","https://openalex.org/W6783487410","https://openalex.org/W6787038602","https://openalex.org/W6787649598","https://openalex.org/W6791074292"],"related_works":["https://openalex.org/W3133700904","https://openalex.org/W2356047318","https://openalex.org/W4388119348","https://openalex.org/W2113142780","https://openalex.org/W2189481835","https://openalex.org/W98467760","https://openalex.org/W3208429855","https://openalex.org/W2363785511","https://openalex.org/W3161060657","https://openalex.org/W3100661441"],"abstract_inverted_index":{"Conversational":[0],"Emotion":[1],"Recognition":[2],"(CER)":[3],"aims":[4],"at":[5],"classifying":[6],"the":[7,37,49,59,97,102,112,116,137,142,170,176,182,192,210,222,227,235,246,250,253],"emotion":[8,20,31],"of":[9,40,51,111,136,173,186,195,234,252],"each":[10],"utterance":[11,212],"in":[12,36],"a":[13,16,79,90,108,122,133,146,156,196,205,231],"conversation.":[14],"For":[15],"target":[17,197,211],"utterance,":[18,198],"its":[19,214],"is":[21,121,217],"jointly":[22,219],"determined":[23],"by":[24,58,78,115],"multiple":[25],"factors,":[26,130],"such":[27],"as":[28,179],"conversation":[29],"topics,":[30],"labels":[32],"and":[33,188,199,213,248],"intra/inter-speaker":[34,98],"influences,":[35],"conversational":[38,117,193],"context":[39,194],"it.":[41],"Then":[42],"an":[43,70],"important":[44],"research":[45],"question":[46],"arises:":[47],"can":[48,161,243],"effects":[50],"these":[52,86],"contextual":[53],"factors":[54],"be":[55,162],"sufficiently":[56],"captured":[57],"current":[60],"CER":[61,76,150,158,165,177,224,255],"models?":[62],"To":[63,140],"answer":[64],"this":[65],"question,":[66],"we":[67,144],"carry":[68],"out":[69],"empirical":[71],"study":[72],"on":[73,96],"four":[74],"representative":[75,254],"models":[77,87],"context-replacement":[80],"methodology.":[81],"The":[82],"results":[83,239],"suggest":[84],"that":[85,125,241],"either":[88],"exhibit":[89],"label-copying":[91],"effect,":[92],"or":[93],"rely":[94],"heavily":[95],"dependency":[99],"structure":[100],"within":[101],"conversation,":[103],"but":[104],"do":[105],"not":[106],"make":[107],"good":[109],"use":[110],"semantics":[113],"carried":[114],"context.":[118,139,215,236],"Thus,":[119],"there":[120],"high":[123],"risk":[124],"they":[126],"overfit":[127],"certain":[128],"single":[129],"yet":[131],"lacking":[132],"holistic":[134],"understanding":[135,233],"semantic":[138,207,232],"tackle":[141],"problem,":[143],"propose":[145],"semantic-guided":[147],"contrastive":[148,201],"context-aware":[149],"method,":[151],"namely":[152],"C3ER,":[153],"to":[154,191,203,229],"augment/regularize":[155],"backbone":[157],"model,":[159,225],"which":[160],"any":[163],"neural":[164],"framework.":[166],"Specifically,":[167],"C3ER":[168,242],"takes":[169],"hidden":[171],"states":[172],"utterances":[174,190],"from":[175],"model":[178,228],"input,":[180],"extracts":[181],"contrast":[183],"pairs":[184],"consisting":[185],"relevant":[187],"irrelevant":[189],"uses":[200],"learning":[202],"establish":[204],"soft":[206],"constraint":[208],"between":[209],"It":[216],"then":[218],"trained":[220],"with":[221],"main":[223],"forcing":[226],"gain":[230],"Extensive":[237],"experimental":[238],"show":[240],"significantly":[244],"boost":[245],"accuracy":[247],"improve":[249],"robustness":[251],"models.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}