{"id":"https://openalex.org/W4389169833","doi":"https://doi.org/10.1109/taslp.2023.3337643","title":"Lost in Context? On the Sense-Wise Variance of Contextualized Word Embeddings","display_name":"Lost in Context? On the Sense-Wise Variance of Contextualized Word Embeddings","publication_year":2023,"publication_date":"2023-11-30","ids":{"openalex":"https://openalex.org/W4389169833","doi":"https://doi.org/10.1109/taslp.2023.3337643"},"language":"en","primary_location":{"id":"doi:10.1109/taslp.2023.3337643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2023.3337643","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"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/ACM Transactions on Audio, Speech, and Language Processing","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/A5044326908","display_name":"Yile Wang","orcid":"https://orcid.org/0000-0001-8705-9598"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yile Wang","raw_affiliation_strings":["Institute for AI Industry Research, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8705-9598","affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100333729","display_name":"Yue Zhang","orcid":"https://orcid.org/0000-0002-5214-2268"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhang","raw_affiliation_strings":["School of Engineering, Westlake University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5214-2268","affiliations":[{"raw_affiliation_string":"School of Engineering, Westlake University, Hangzhou, China","institution_ids":["https://openalex.org/I3133055985"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5044326908"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.852,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79472257,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"32","issue":null,"first_page":"639","last_page":"650"},"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":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9930999875068665,"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/polysemy","display_name":"Polysemy","score":0.7940205335617065},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6557361483573914},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6485919952392578},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5803743600845337},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5482339859008789},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5358738899230957},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5286284685134888},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.49497026205062866},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4465491771697998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4402760863304138},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.35203906893730164}],"concepts":[{"id":"https://openalex.org/C2780276568","wikidata":"https://www.wikidata.org/wiki/Q191928","display_name":"Polysemy","level":2,"score":0.7940205335617065},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6557361483573914},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6485919952392578},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5803743600845337},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5482339859008789},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5358738899230957},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5286284685134888},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49497026205062866},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4465491771697998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4402760863304138},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.35203906893730164},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taslp.2023.3337643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taslp.2023.3337643","pdf_url":null,"source":{"id":"https://openalex.org/S4210169297","display_name":"IEEE/ACM Transactions on Audio Speech and Language Processing","issn_l":"2329-9290","issn":["2329-9290","2329-9304"],"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/ACM Transactions on Audio, Speech, and Language Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8299999833106995,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G8869030430","display_name":null,"funder_award_id":"61976180","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W2031026221","https://openalex.org/W2064675550","https://openalex.org/W2131540451","https://openalex.org/W2144578941","https://openalex.org/W2250539671","https://openalex.org/W2525778437","https://openalex.org/W2740782137","https://openalex.org/W2884097483","https://openalex.org/W2915128308","https://openalex.org/W2923014074","https://openalex.org/W2948947170","https://openalex.org/W2952682849","https://openalex.org/W2962739339","https://openalex.org/W2962784628","https://openalex.org/W2963691697","https://openalex.org/W2964110616","https://openalex.org/W2964204621","https://openalex.org/W2964303116","https://openalex.org/W2965373594","https://openalex.org/W2970476646","https://openalex.org/W2970641574","https://openalex.org/W2970773744","https://openalex.org/W2970925903","https://openalex.org/W2971296520","https://openalex.org/W2972324944","https://openalex.org/W2979826702","https://openalex.org/W2988217457","https://openalex.org/W3034503989","https://openalex.org/W3035153870","https://openalex.org/W3035407756","https://openalex.org/W3098614164","https://openalex.org/W3099624838","https://openalex.org/W3105858864","https://openalex.org/W3135970112","https://openalex.org/W3158662732","https://openalex.org/W3174583150","https://openalex.org/W3211445278","https://openalex.org/W4288631803","https://openalex.org/W4295168876","https://openalex.org/W4367190805","https://openalex.org/W6605500565","https://openalex.org/W6636510571","https://openalex.org/W6636649193","https://openalex.org/W6691470440","https://openalex.org/W6691618750","https://openalex.org/W6727690538","https://openalex.org/W6732295589","https://openalex.org/W6739901393","https://openalex.org/W6752342493","https://openalex.org/W6755207826","https://openalex.org/W6757635932","https://openalex.org/W6757883768","https://openalex.org/W6761052584","https://openalex.org/W6762392948","https://openalex.org/W6762715600","https://openalex.org/W6763701032","https://openalex.org/W6766673545","https://openalex.org/W6767594909","https://openalex.org/W6768021236","https://openalex.org/W6770578294","https://openalex.org/W6772825485","https://openalex.org/W6773996589","https://openalex.org/W6779068807","https://openalex.org/W6790715162","https://openalex.org/W6791108739","https://openalex.org/W6794419084","https://openalex.org/W6852229780"],"related_works":["https://openalex.org/W2376040010","https://openalex.org/W2613880225","https://openalex.org/W2788559978","https://openalex.org/W2358036664","https://openalex.org/W2891304714","https://openalex.org/W4385239993","https://openalex.org/W2310152915","https://openalex.org/W2362895247","https://openalex.org/W4285531126","https://openalex.org/W2600654830"],"abstract_inverted_index":{"Contextualized":[0],"word":[1,87,122,140,173],"embeddings":[2,84,101],"in":[3,92,148,171],"language":[4,210],"models":[5],"have":[6,127],"given":[7],"much":[8,81],"advance":[9],"to":[10,32,152,167],"NLP.":[11],"Intuitively,":[12],"sentential":[13],"information":[14],"is":[15],"integrated":[16],"into":[17,66],"the":[18,33,40,53,70,82,96,114,131,145,180,186,200],"representation":[19],"of":[20,35,85,121,133,182,188],"words,":[21],"which":[22,37],"can":[23,102,198],"help":[24],"model":[25],"polysemy.":[26],"However,":[27],"context":[28],"sensitivity":[29,50],"also":[30,162],"leads":[31],"variance":[34,132],"representations,":[36,58],"may":[38],"break":[39],"semantic":[41],"consistency":[42],"for":[43,109],"synonyms.":[44],"Previous":[45],"works":[46],"that":[47,99,139,194],"investigate":[48,179],"contextualized":[49,83,100],"focus":[51],"on":[52,130,185],"<italic":[54,72],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[55,73],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">token</i>":[56],"level":[57],"while":[59,205],"we":[60,78,137,178],"are":[61,142],"taking":[62],"a":[63,159,164],"deeper":[64],"dive":[65],"exploring":[67],"representations":[68,141],"at":[69],"fine-grained":[71],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">sense</i>":[74],"level.":[75],"In":[76,117],"particular,":[77],"quantify":[79],"how":[80],"each":[86],"sense":[88,134,174],"vary":[89],"across":[90,106],"contexts":[91,150],"typical":[93],"pre-trained":[94],"models,":[95],"results":[97,192],"show":[98,193],"be":[103,153],"highly":[104],"consistent":[105],"contexts,":[107],"even":[108],"two":[110],"different":[111,149,189],"words":[112,147],"with":[113],"same":[115],"sense.":[116],"addition,":[118],"part-of-speech,":[119],"number":[120],"senses,":[123],"and":[124,161,202],"sentence":[125],"length":[126],"an":[128],"influence":[129,181],"representations.":[135],"Interestingly,":[136],"find":[138],"position-biased,":[143],"where":[144],"first":[146],"tend":[151],"more":[154],"similar.":[155],"We":[156],"analyze":[157],"such":[158,169,195],"phenomenon":[160],"propose":[163],"prompt-augmentation":[165],"method":[166],"alleviate":[168],"bias":[170],"distance-based":[172],"disambiguation":[175],"settings.":[176],"Finally,":[177],"sense-level":[183],"pre-training":[184],"performance":[187],"downstream":[190],"tasks,":[191,204],"external":[196],"tasks":[197],"improve":[199],"sense-":[201],"syntactic-related":[203],"not":[206],"necessarily":[207],"benefiting":[208],"general":[209],"understanding":[211],"tasks.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
