{"id":"https://openalex.org/W3176117839","doi":"https://doi.org/10.1145/3457682.3457763","title":"K-CSRL: Knowledge Enhanced Conversational Semantic Role Labeling","display_name":"K-CSRL: Knowledge Enhanced Conversational Semantic Role Labeling","publication_year":2021,"publication_date":"2021-02-26","ids":{"openalex":"https://openalex.org/W3176117839","doi":"https://doi.org/10.1145/3457682.3457763","mag":"3176117839"},"language":"en","primary_location":{"id":"doi:10.1145/3457682.3457763","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3457682.3457763","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Machine Learning and Computing","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/A5061529035","display_name":"Boyu He","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Boyu He","raw_affiliation_strings":["Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034126390","display_name":"Han Wu","orcid":"https://orcid.org/0000-0002-8008-064X"},"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":"Han Wu","raw_affiliation_strings":["City University of Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049701424","display_name":"Congduan Li","orcid":"https://orcid.org/0000-0003-0495-332X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Congduan Li","raw_affiliation_strings":["Sun Yat-Sen University, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035185924","display_name":"Linqi Song","orcid":"https://orcid.org/0000-0003-2756-4984"},"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":"Linqi Song","raw_affiliation_strings":["City University of Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108067228","display_name":"Weigang Chen","orcid":"https://orcid.org/0000-0002-4880-8186"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weigang Chen","raw_affiliation_strings":["Tianjin University, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5061529035"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.4079,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67959801,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"530","last_page":"535"},"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":0.9998999834060669,"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/T12031","display_name":"Speech and dialogue systems","score":0.9868999719619751,"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":0.8292274475097656},{"id":"https://openalex.org/keywords/semantic-role-labeling","display_name":"Semantic role labeling","score":0.7041694521903992},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6643198132514954},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5989592671394348},{"id":"https://openalex.org/keywords/predicate","display_name":"Predicate (mathematical logic)","score":0.58690345287323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.568344235420227},{"id":"https://openalex.org/keywords/anaphora","display_name":"Anaphora (linguistics)","score":0.5344113111495972},{"id":"https://openalex.org/keywords/argument","display_name":"Argument (complex analysis)","score":0.4324856102466583},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.26811420917510986}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8292274475097656},{"id":"https://openalex.org/C67277372","wikidata":"https://www.wikidata.org/wiki/Q7449085","display_name":"Semantic role labeling","level":3,"score":0.7041694521903992},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6643198132514954},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5989592671394348},{"id":"https://openalex.org/C140146324","wikidata":"https://www.wikidata.org/wiki/Q1144319","display_name":"Predicate (mathematical logic)","level":2,"score":0.58690345287323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.568344235420227},{"id":"https://openalex.org/C2781449363","wikidata":"https://www.wikidata.org/wiki/Q156751","display_name":"Anaphora (linguistics)","level":3,"score":0.5344113111495972},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.4324856102466583},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.26811420917510986},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3457682.3457763","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3457682.3457763","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 13th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.49000000953674316,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2103076621","https://openalex.org/W2133564696","https://openalex.org/W2489487449","https://openalex.org/W2599674900","https://openalex.org/W2604368306","https://openalex.org/W2620787630","https://openalex.org/W2751936342","https://openalex.org/W2896457183","https://openalex.org/W2946345909","https://openalex.org/W2949769095","https://openalex.org/W2952889708","https://openalex.org/W2953356739","https://openalex.org/W2964223283","https://openalex.org/W2973840669","https://openalex.org/W2998385486","https://openalex.org/W3089938501","https://openalex.org/W4211156271","https://openalex.org/W7071655546"],"related_works":["https://openalex.org/W2377185541","https://openalex.org/W2378553216","https://openalex.org/W2462900308","https://openalex.org/W2096992842","https://openalex.org/W2362026785","https://openalex.org/W2171044983","https://openalex.org/W2914151043","https://openalex.org/W2358573072","https://openalex.org/W1990677253","https://openalex.org/W2121717955"],"abstract_inverted_index":{"Semantic":[0,48],"role":[1],"labeling":[2],"(SRL)":[3],"is":[4,96],"widely":[5],"used":[6],"to":[7,25,42,119],"extract":[8],"predicate-argument":[9],"pairs":[10],"from":[11,158],"sentences.":[12],"Traditional":[13],"SRL":[14,155],"methods":[15],"can":[16,156],"perform":[17],"well":[18],"on":[19,134],"the":[20,71,74,88,97,104,112,147],"single":[21],"sentence":[22],"but":[23,52],"fail":[24],"work":[26,38],"in":[27,78,82],"dialogue":[28,154],"scenario":[29],"where":[30],"ellipsis":[31],"and":[32,110],"anaphora":[33],"frequently":[34],"occurs.":[35],"Some":[36],"research":[37],"has":[39,68],"been":[40],"proposed":[41,132],"solve":[43],"this":[44,92,115],"problem,":[45],"i.e.":[46],"Conversational":[47],"Role":[49],"Labeling":[50],"(CSRL),":[51],"there":[53],"are":[54,76],"still":[55],"huge":[56],"room":[57],"for":[58],"improvements.":[59],"The":[60],"error":[61,95],"case":[62],"study":[63],"of":[64,73,91,94,99],"BERT-based":[65],"CSRL":[66],"model":[67,106,133,142],"shown":[69],"that":[70,103,140,153],"majority":[72],"errors":[75],"observed":[77],"boundary":[79],"matching,":[80],"especially":[81],"entity":[83],"mention":[84],"detection.":[85],"We":[86,129],"think":[87],"premier":[89],"cause":[90],"kind":[93],"deficiency":[98],"external":[100,121,159],"knowledge":[101,122,144],"such":[102],"ill-informed":[105],"cannot":[107],"correctly":[108],"capture":[109],"correlate":[111],"entities.":[113],"To":[114],"end,":[116],"we":[117],"propose":[118],"incorporate":[120],"into":[123],"BERT":[124],"using":[125],"visible":[126],"masking":[127],"strategy.":[128],"evaluate":[130],"our":[131,141],"DuConv":[135],"dataset.":[136],"Experimental":[137],"results":[138],"show":[139],"with":[143],"enhancement":[145],"outperforms":[146],"benchmarks.":[148],"Further":[149],"analysis":[150],"also":[151],"demonstrates":[152],"benefit":[157],"knowledge.":[160]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}