{"id":"https://openalex.org/W2937553196","doi":"https://doi.org/10.1109/icassp.2019.8683017","title":"Investigation of Sampling Techniques for Maximum Entropy Language Modeling Training","display_name":"Investigation of Sampling Techniques for Maximum Entropy Language Modeling Training","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2937553196","doi":"https://doi.org/10.1109/icassp.2019.8683017","mag":"2937553196"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8683017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100329117","display_name":"Xie Chen","orcid":"https://orcid.org/0000-0001-5801-2571"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xie Chen","raw_affiliation_strings":["Microsoft, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100400217","display_name":"Jun Zhang","orcid":"https://orcid.org/0000-0001-7835-9871"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Zhang","raw_affiliation_strings":["Microsoft, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048981110","display_name":"Tasos Anastasakos","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tasos Anastasakos","raw_affiliation_strings":["Microsoft, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109977451","display_name":"Fil Alleva","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fil Alleva","raw_affiliation_strings":["Microsoft, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1446,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55752517,"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":"7240","last_page":"7244"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9990000128746033,"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":0.9990000128746033,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9977999925613403,"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/T10320","display_name":"Neural Networks and Applications","score":0.9976999759674072,"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/softmax-function","display_name":"Softmax function","score":0.8171018958091736},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7940517663955688},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.5968281030654907},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5743924379348755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5528952479362488},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5315515995025635},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5221236348152161},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5107266902923584},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5015971660614014},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4068431258201599},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39181333780288696},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32639074325561523}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8171018958091736},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940517663955688},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.5968281030654907},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5743924379348755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5528952479362488},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5315515995025635},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5221236348152161},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5107266902923584},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5015971660614014},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4068431258201599},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39181333780288696},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32639074325561523},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8683017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8683017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W122584218","https://openalex.org/W1520465330","https://openalex.org/W1965154800","https://openalex.org/W1974515274","https://openalex.org/W1996903695","https://openalex.org/W2001792610","https://openalex.org/W2050971845","https://openalex.org/W2100714283","https://openalex.org/W2119400430","https://openalex.org/W2120861206","https://openalex.org/W2152790380","https://openalex.org/W2152808281","https://openalex.org/W2259472270","https://openalex.org/W2289691127","https://openalex.org/W2296014541","https://openalex.org/W2311729496","https://openalex.org/W2463033603","https://openalex.org/W2611669587","https://openalex.org/W2624076736","https://openalex.org/W2747467128","https://openalex.org/W2747886799","https://openalex.org/W2802081162","https://openalex.org/W2950797609","https://openalex.org/W2951714314","https://openalex.org/W2963932686","https://openalex.org/W2963963412","https://openalex.org/W6604992714","https://openalex.org/W6632248436","https://openalex.org/W6678040779","https://openalex.org/W6682948231","https://openalex.org/W6688570741","https://openalex.org/W6692563993","https://openalex.org/W6697421373","https://openalex.org/W6698703632","https://openalex.org/W6884717427"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W3108503355","https://openalex.org/W4226420367","https://openalex.org/W2962876041","https://openalex.org/W3090555870","https://openalex.org/W3022820045","https://openalex.org/W1773599773","https://openalex.org/W2268150819","https://openalex.org/W2047632477"],"abstract_inverted_index":{"Maximum":[0],"entropy":[1],"language":[2,97],"models":[3,8],"(MaxEnt":[4],"LMs)":[5],"are":[6,10,23,116],"log-linear":[7],"which":[9],"able":[11],"to":[12,34,69,77,162],"incorporate":[13],"various":[14],"hand-crafted":[15],"features":[16],"and":[17,72,113,131,143],"non-linguistic":[18],"information.":[19],"Standard":[20],"MaxEnt":[21,50,56,64,120,146,156,177],"LMs":[22,51,65],"computationally":[24],"heavy":[25],"for":[26,118,155],"tasks":[27],"with":[28],"a":[29],"large":[30],"vocabulary":[31],"size":[32],"due":[33],"the":[35,40,59,84,92,119,126,138,170],"expensive":[36],"normalization":[37],"computation":[38],"in":[39,90,99],"denominator.":[41],"To":[42,165],"address":[43],"this":[44,100,168],"issue,":[45],"most":[46],"recent":[47,85],"works":[48],"on":[49,125,145,176],"have":[52],"used":[53,104],"class":[54,62],"based":[55,63],"LMs.":[57],"However,":[58,149],"performance":[60],"of":[61,87,94,140],"might":[66],"be":[67],"sensitive":[68],"word":[70,80],"clustering":[71],"it":[73],"is":[74,152,169],"also":[75],"time-consuming":[76],"generate":[78],"high-quality":[79],"classes.":[81],"Motivated":[82],"by":[83],"success":[86],"sampling":[88,105,151,174],"techniques":[89,175],"accelerating":[91],"training":[93,158],"neural":[95],"network":[96],"models,":[98],"paper,":[101],"three":[102],"widely":[103],"techniques,":[106],"importance":[107,150],"sampling,":[108],"noise":[109],"contrastive":[110],"estimation":[111],"(NCE)":[112],"sampled":[114,141,163],"softmax,":[115],"investigated":[117],"LM":[121,147,157,178],"training.":[122,148,179],"Experimental":[123],"results":[124],"Google":[127],"One":[128],"Billion":[129],"corpus":[130],"an":[132],"internal":[133],"speech":[134],"recognition":[135],"system":[136],"demonstrate":[137],"effectiveness":[139],"softmax":[142],"NCE":[144],"not":[153],"effective":[154],"despite":[159],"its":[160],"similarity":[161],"softmax.":[164],"our":[166],"knowledge,":[167],"first":[171],"work":[172],"applying":[173]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
