{"id":"https://openalex.org/W4312266776","doi":"https://doi.org/10.1109/taffc.2022.3218648","title":"A Reinforcement Learning Based Two-Stage Model for Emotion Cause Pair Extraction","display_name":"A Reinforcement Learning Based Two-Stage Model for Emotion Cause Pair Extraction","publication_year":2022,"publication_date":"2022-11-04","ids":{"openalex":"https://openalex.org/W4312266776","doi":"https://doi.org/10.1109/taffc.2022.3218648"},"language":"en","primary_location":{"id":"doi:10.1109/taffc.2022.3218648","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2022.3218648","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/A5071933192","display_name":"Xinhong Chen","orcid":"https://orcid.org/0000-0002-8563-148X"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xinhong Chen","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-8563-148X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404176","display_name":"Qing Li","orcid":"https://orcid.org/0000-0003-3370-471X"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Qing Li","raw_affiliation_strings":["Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0003-3370-471X","affiliations":[{"raw_affiliation_string":"Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076059022","display_name":"Zongxi Li","orcid":"https://orcid.org/0000-0002-1708-7099"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zongxi Li","raw_affiliation_strings":["School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-1708-7099","affiliations":[{"raw_affiliation_string":"School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013151488","display_name":"Haoran Xie","orcid":"https://orcid.org/0000-0003-0965-3617"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haoran Xie","raw_affiliation_strings":["Department of Computing and Decision Sciences, Tuen Mun, Lingnan University, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0003-0965-3617","affiliations":[{"raw_affiliation_string":"Department of Computing and Decision Sciences, Tuen Mun, Lingnan University, Hong Kong","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072482402","display_name":"Fu Lee Wang","orcid":"https://orcid.org/0000-0002-3976-0053"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu Lee Wang","raw_affiliation_strings":["School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-3976-0053","affiliations":[{"raw_affiliation_string":"School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Hong Kong","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100356291","display_name":"Jianping Wang","orcid":"https://orcid.org/0000-0002-9318-1482"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jianping Wang","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-9318-1482","affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5746,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.85892924,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"14","issue":"3","first_page":"1779","last_page":"1790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","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/T10028","display_name":"Topic Modeling","score":0.995199978351593,"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/T11644","display_name":"Spam and Phishing Detection","score":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7763672471046448},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6928935050964355},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6833881139755249},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6693905591964722},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6281282901763916},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.619964599609375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5259725451469421},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08039656281471252}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7763672471046448},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6928935050964355},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6833881139755249},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6693905591964722},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6281282901763916},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.619964599609375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5259725451469421},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08039656281471252},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taffc.2022.3218648","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2022.3218648","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":[{"id":"https://metadata.un.org/sdg/10","score":0.7799999713897705,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1990756099","https://openalex.org/W2119717200","https://openalex.org/W2155027007","https://openalex.org/W2161624371","https://openalex.org/W2413899610","https://openalex.org/W2473640056","https://openalex.org/W2531987334","https://openalex.org/W2562539671","https://openalex.org/W2605323333","https://openalex.org/W2884287800","https://openalex.org/W2890362944","https://openalex.org/W2892381115","https://openalex.org/W2896457183","https://openalex.org/W2906728375","https://openalex.org/W2921583943","https://openalex.org/W2951124019","https://openalex.org/W2962913831","https://openalex.org/W2964181805","https://openalex.org/W2964829511","https://openalex.org/W2966024403","https://openalex.org/W2982132746","https://openalex.org/W2988456275","https://openalex.org/W3008734205","https://openalex.org/W3020594048","https://openalex.org/W3035101819","https://openalex.org/W3035327313","https://openalex.org/W3035563474","https://openalex.org/W3049607010","https://openalex.org/W3081987387","https://openalex.org/W3100743427","https://openalex.org/W3105195400","https://openalex.org/W3113863090","https://openalex.org/W3115801429","https://openalex.org/W3116756642","https://openalex.org/W3120854948","https://openalex.org/W3133852917","https://openalex.org/W3165439169","https://openalex.org/W3172474223","https://openalex.org/W3190697745","https://openalex.org/W3200102354","https://openalex.org/W3210828003","https://openalex.org/W3211349157","https://openalex.org/W3214534511","https://openalex.org/W4205999865","https://openalex.org/W4226108406","https://openalex.org/W4244167344","https://openalex.org/W4281400927","https://openalex.org/W4284884608","https://openalex.org/W6683204974","https://openalex.org/W6683754746","https://openalex.org/W6755207826","https://openalex.org/W6757817853","https://openalex.org/W6774500908"],"related_works":["https://openalex.org/W4296474751","https://openalex.org/W3074294383","https://openalex.org/W1485630101","https://openalex.org/W3153007185","https://openalex.org/W2498017833","https://openalex.org/W4206669594","https://openalex.org/W2961085424","https://openalex.org/W4319083788","https://openalex.org/W3123920941","https://openalex.org/W2983785000"],"abstract_inverted_index":{"Recently,":[0],"many":[1,31],"efforts":[2],"have":[3,51],"been":[4,52],"devoted":[5],"to":[6,55,92,123,133,200],"promoting":[7],"the":[8,28,34,43,63,66,87,94,125,135,161,175,181,185,194,202,206,213,219,223,231,235],"Emotion-Cause":[9],"Pair":[10],"Extraction":[11],"(ECPE)":[12],"task,":[13],"as":[14,42,70,166,209],"jointly":[15],"extracting":[16],"emotions":[17,29,104],"and":[18,82,105,128,148,230],"their":[19,56],"causes":[20,106],"is":[21],"considered":[22],"more":[23],"helpful":[24],"than":[25],"only":[26],"identifying":[27],"in":[30,205,212],"applications.":[32],"Among":[33],"existing":[35,88],"efforts,":[36],"end-to-end":[37,89],"approaches":[38,90],"are":[39,80,109],"getting":[40],"popular":[41],"main":[44],"trend,":[45],"while":[46],"others":[47],"like":[48],"pipeline":[49,67],"models":[50],"overlooked":[53],"due":[54],"potential":[57],"issues":[58],"of":[59,65,103,114,163,225,238],"cascading":[60,136,240],"errors.":[61],"Nevertheless,":[62],"advantages":[64],"models,":[68],"such":[69],"logically":[71],"dividing":[72],"a":[73,119],"complicated":[74],"task":[75,127],"into":[76],"multiple":[77,101],"easier":[78],"subtasks,":[79],"underestimated":[81],"not":[83],"well":[84],"exploited.":[85],"Moreover,":[86],"fail":[91],"capture":[93],"implicit":[95],"co-occurrence":[96],"or":[97],"exclusion":[98],"patterns":[99],"between":[100],"pairs":[102],"since":[107],"they":[108],"extracted":[110],"independently.":[111],"In":[112,139],"view":[113],"these":[115],"limitations,":[116],"we":[117],"propose":[118],"novel":[120],"two-stage":[121,142,228],"model":[122,143,171,186,199],"address":[124],"ECPE":[126],"incorporate":[129],"reinforcement":[130],"learning":[131],"(RL)":[132],"tackle":[134],"error":[137,162,176],"issue.":[138],"particular,":[140],"our":[141,170,198,226],"first":[144],"detects":[145],"emotion":[146,156],"clauses":[147,152],"then":[149],"recognizes":[150],"cause":[151],"for":[153,190],"each":[154,164,178],"detected":[155],"clause":[157],"sequentially.":[158],"By":[159],"representing":[160],"decision":[165],"an":[167],"explicit":[168],"reward,":[169],"clearly":[172],"knows":[173],"how":[174],"at":[177],"stage":[179],"affects":[180],"final":[182],"performance,":[183],"hence":[184],"can":[187],"adjust":[188],"itself":[189],"better":[191],"performance.":[192],"Furthermore,":[193],"sequential":[195],"prediction":[196],"enables":[197],"use":[201],"results":[203],"achieved":[204],"previous":[207],"stages":[208],"auxiliary":[210],"information":[211],"subsequent":[214],"stages.":[215],"Extensive":[216],"experiments":[217],"on":[218],"benchmark":[220],"dataset":[221],"demonstrate":[222],"effectiveness":[224],"proposed":[227],"model,":[229],"ablation":[232],"comparison":[233],"shows":[234],"promising":[236],"effect":[237],"reducing":[239],"errors":[241],"by":[242],"incorporating":[243],"RL.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}