{"id":"https://openalex.org/W2889769646","doi":"https://doi.org/10.18653/v1/d18-1408","title":"Phrase-level Self-Attention Networks for Universal Sentence Encoding","display_name":"Phrase-level Self-Attention Networks for Universal Sentence Encoding","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2889769646","doi":"https://doi.org/10.18653/v1/d18-1408","mag":"2889769646"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1408","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1408","pdf_url":"https://www.aclweb.org/anthology/D18-1408.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1408.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101796417","display_name":"Wei Wu","orcid":"https://orcid.org/0000-0001-6572-8471"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wu","raw_affiliation_strings":["MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025565222","display_name":"Houfeng Wang","orcid":"https://orcid.org/0000-0001-7130-1589"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210157323","display_name":"South China Institute of Collaborative Innovation","ror":"https://ror.org/04jnpk588","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210157323","https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Houfeng Wang","raw_affiliation_strings":["Collaborative Innovation Center for Language Ability, Xuzhou, Jiangsu, 221009, China","MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center for Language Ability, Xuzhou, Jiangsu, 221009, China","institution_ids":["https://openalex.org/I4210157323"]},{"raw_affiliation_string":"MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115666590","display_name":"Tianyu Liu","orcid":"https://orcid.org/0000-0003-0774-8663"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyu Liu","raw_affiliation_strings":["MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113130010","display_name":"Shuming Ma","orcid":"https://orcid.org/0000-0003-1091-1206"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuming Ma","raw_affiliation_strings":["MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MOE Key Lab of Computational Linguistics, Peking University, Beijing, 100871, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.5755,"has_fulltext":true,"cited_by_count":49,"citation_normalized_percentile":{"value":0.96648163,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3729","last_page":"3738"},"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":0.9994999766349792,"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":0.9871000051498413,"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/computer-science","display_name":"Computer science","score":0.8161956071853638},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.7272238731384277},{"id":"https://openalex.org/keywords/phrase","display_name":"Phrase","score":0.7248276472091675},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6715818643569946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6180217862129211},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5590594410896301},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5308146476745605},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5043648481369019},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.15678539872169495}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8161956071853638},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.7272238731384277},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.7248276472091675},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6715818643569946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6180217862129211},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5590594410896301},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5308146476745605},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5043648481369019},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.15678539872169495},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1408","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1408","pdf_url":"https://www.aclweb.org/anthology/D18-1408.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d18-1408","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1408","pdf_url":"https://www.aclweb.org/anthology/D18-1408.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G118205048","display_name":null,"funder_award_id":"2017YFB1002101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8148101051","display_name":"\u6c49\u8bed\u8ba4\u77e5\u52a0\u5de5\u673a\u5236\u4e0e\u8ba1\u7b97\u6a21\u578b\u7814\u7a76","funder_award_id":"61433015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8356743227","display_name":null,"funder_award_id":"2017YFB1002101","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2889769646.pdf","grobid_xml":"https://content.openalex.org/works/W2889769646.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1486649854","https://openalex.org/W1533861849","https://openalex.org/W1681397005","https://openalex.org/W1793121960","https://openalex.org/W1832693441","https://openalex.org/W1840435438","https://openalex.org/W1879966306","https://openalex.org/W1924770834","https://openalex.org/W2064675550","https://openalex.org/W2095705004","https://openalex.org/W2097606805","https://openalex.org/W2107878631","https://openalex.org/W2120615054","https://openalex.org/W2140679639","https://openalex.org/W2153579005","https://openalex.org/W2171590421","https://openalex.org/W2250539671","https://openalex.org/W2251939518","https://openalex.org/W2252215182","https://openalex.org/W2267186426","https://openalex.org/W2413794162","https://openalex.org/W2415204069","https://openalex.org/W2597655663","https://openalex.org/W2752104716","https://openalex.org/W2790235966","https://openalex.org/W2951008357","https://openalex.org/W2951528484","https://openalex.org/W2962685628","https://openalex.org/W2962998327","https://openalex.org/W2963241825","https://openalex.org/W2963285578","https://openalex.org/W2963355447","https://openalex.org/W2963403868","https://openalex.org/W2963580443","https://openalex.org/W2963872035","https://openalex.org/W2963918774","https://openalex.org/W2963973721","https://openalex.org/W2964165804","https://openalex.org/W2964189376","https://openalex.org/W3003519915","https://openalex.org/W3016169217","https://openalex.org/W4285719527","https://openalex.org/W4294027320","https://openalex.org/W4294170691","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2039546652","https://openalex.org/W2012262991","https://openalex.org/W2373794620","https://openalex.org/W2060629350","https://openalex.org/W2357294589","https://openalex.org/W2386861027","https://openalex.org/W2349302580","https://openalex.org/W2375873920","https://openalex.org/W2390154576","https://openalex.org/W2296205523"],"abstract_inverted_index":{"Universal":[0],"sentence":[1,20,68,144,177,183],"encoding":[2,21],"is":[3,74,135],"a":[4,91,120,124,171],"hot":[5],"topic":[6],"in":[7,18,36,119,156],"recent":[8],"NLP":[9,174],"research.":[10],"Attention":[11],"mechanism":[12,107],"has":[13],"been":[14],"an":[15],"integral":[16],"part":[17],"many":[19],"models,":[22],"allowing":[23],"the":[24,32,37,59,71,98,103,126,133,138,143,147,157,166],"models":[25,41,64],"to":[26,48,93,108],"capture":[27,94],"context":[28,95,116],"dependencies":[29,96,117],"regardless":[30],"of":[31,62,142,173],"distance":[33],"between":[34],"elements":[35],"sequence.":[38],"Fully":[39],"attention-based":[40],"have":[42],"recently":[43],"attracted":[44],"enormous":[45],"interest":[46],"due":[47],"their":[49,63],"highly":[50],"parallelizable":[51],"computation":[52],"and":[53,70,101,182],"significantly":[54],"less":[55],"training":[56],"time.":[57],"However,":[58],"memory":[60,105,127],"consumption":[61,128],"grows":[65],"quadratically":[66],"with":[67,114],"length,":[69],"syntactic":[72,150],"information":[73,151],"neglected.":[75],"To":[76],"this":[77],"end,":[78],"we":[79],"propose":[80],"Phrase-level":[81],"Self-Attention":[82],"Networks":[83],"(PSAN)":[84],"that":[85,162],"perform":[86],"self-attention":[87,134],"across":[88,170],"words":[89],"inside":[90],"phrase":[92,99,139],"at":[97,137],"level,":[100],"use":[102],"gated":[104],"updating":[106],"refine":[109],"each":[110],"word's":[111],"representation":[112],"hierarchically":[113],"longer-term":[115],"captured":[118],"larger":[121],"phrase.":[122],"As":[123],"result,":[125],"can":[129,152,164],"be":[130,153],"reduced":[131],"because":[132],"performed":[136],"level":[140],"instead":[141],"level.":[145],"At":[146],"same":[148],"time,":[149],"easily":[154],"integrated":[155],"model.":[158],"Experiment":[159],"results":[160],"show":[161],"PSAN":[163],"achieve":[165],"state-ofthe-art":[167],"transfer":[168],"performance":[169],"plethora":[172],"tasks":[175],"including":[176],"classification,":[178],"natural":[179],"language":[180],"inference":[181],"textual":[184],"similarity.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":14}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
