{"id":"https://openalex.org/W4295887126","doi":"https://doi.org/10.48550/arxiv.2209.05869","title":"Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching","display_name":"Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching","publication_year":2022,"publication_date":"2022-09-13","ids":{"openalex":"https://openalex.org/W4295887126","doi":"https://doi.org/10.48550/arxiv.2209.05869"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2209.05869","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.05869","pdf_url":"https://arxiv.org/pdf/2209.05869","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2209.05869","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064389132","display_name":"Kunbo Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ding, Kunbo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100668786","display_name":"Weijie Liu","orcid":"https://orcid.org/0000-0002-8023-9913"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Weijie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088688674","display_name":"Yuejian Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Yuejian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100631152","display_name":"Zhe Zhao","orcid":"https://orcid.org/0000-0003-4189-3258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Zhe","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071290194","display_name":"Qi Ju","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ju, Qi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100715830","display_name":"Xuefeng Yang","orcid":"https://orcid.org/0000-0002-3832-2422"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xuefeng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5064389132"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9972000122070312,"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":0.9972000122070312,"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":0.996399998664856,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.993399977684021,"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":0.7814593315124512},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7682689428329468},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6794009804725647},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.6471261978149414},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5499255061149597},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5257495045661926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46095943450927734},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4180450439453125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40352967381477356},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3534221053123474},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.193773090839386},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11225572228431702},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.09318733215332031},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.08182874321937561},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07960951328277588}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7814593315124512},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7682689428329468},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6794009804725647},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6471261978149414},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5499255061149597},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5257495045661926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46095943450927734},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4180450439453125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40352967381477356},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3534221053123474},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.193773090839386},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11225572228431702},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.09318733215332031},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.08182874321937561},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07960951328277588},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2209.05869","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.05869","pdf_url":"https://arxiv.org/pdf/2209.05869","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2209.05869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2209.05869","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2209.05869","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.05869","pdf_url":"https://arxiv.org/pdf/2209.05869","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1001352512","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138"],"abstract_inverted_index":{"Previous":[0],"studies":[1],"have":[2],"proved":[3],"that":[4,98],"cross-lingual":[5,17,55,70],"knowledge":[6,56],"distillation":[7,57,62],"can":[8,101],"significantly":[9],"improve":[10],"the":[11,22,91,103,114],"performance":[12,34,86,115],"of":[13,105],"pre-trained":[14],"models":[15],"for":[16,64],"similarity":[18],"matching":[19],"tasks.":[20],"However,":[21],"student":[23],"model":[24],"needs":[25],"to":[26,42,45,84],"be":[27,43],"large":[28],"in":[29],"this":[30,50],"operation.":[31],"Otherwise,":[32],"its":[33],"will":[35],"drop":[36],"sharply,":[37],"thus":[38],"making":[39],"it":[40],"impractical":[41],"deployed":[44],"memory-limited":[46],"devices.":[47],"To":[48],"address":[49],"issue,":[51],"we":[52],"delve":[53],"into":[54],"and":[58,78,107],"propose":[59],"a":[60,66],"multi-stage":[61],"framework":[63],"constructing":[65],"small-size":[67],"but":[68],"high-performance":[69],"model.":[71],"In":[72],"our":[73,99],"framework,":[74],"contrastive":[75],"learning,":[76],"bottleneck,":[77],"parameter":[79],"recurrent":[80],"strategies":[81],"are":[82],"combined":[83],"prevent":[85],"from":[87],"being":[88],"compromised":[89],"during":[90],"compression":[92],"process.":[93],"The":[94],"experimental":[95],"results":[96],"demonstrate":[97],"method":[100],"compress":[102],"size":[104],"XLM-R":[106],"MiniLM":[108],"by":[109,119],"more":[110],"than":[111],"50\\%,":[112],"while":[113],"is":[116],"only":[117],"reduced":[118],"about":[120],"1%.":[121]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2022-09-16T00:00:00"}
