{"id":"https://openalex.org/W3022089060","doi":"https://doi.org/10.1109/access.2020.2991484","title":"Intention Detection Based on Siamese Neural Network With Triplet Loss","display_name":"Intention Detection Based on Siamese Neural Network With Triplet Loss","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3022089060","doi":"https://doi.org/10.1109/access.2020.2991484","mag":"3022089060"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2991484","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2991484","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09082602.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09082602.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071943346","display_name":"Fuji Ren","orcid":"https://orcid.org/0000-0003-4860-9184"},"institutions":[{"id":"https://openalex.org/I922474255","display_name":"Tokushima University","ror":"https://ror.org/044vy1d05","country_code":"JP","type":"education","lineage":["https://openalex.org/I922474255"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Fuji Ren","raw_affiliation_strings":["Faculty of Engineering, Tokushima University, Tokushima, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Tokushima University, Tokushima, Japan","institution_ids":["https://openalex.org/I922474255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000877046","display_name":"Siyuan Xue","orcid":"https://orcid.org/0000-0002-6367-0955"},"institutions":[{"id":"https://openalex.org/I922474255","display_name":"Tokushima University","ror":"https://ror.org/044vy1d05","country_code":"JP","type":"education","lineage":["https://openalex.org/I922474255"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Siyuan Xue","raw_affiliation_strings":["Faculty of Engineering, Tokushima University, Tokushima, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Tokushima University, Tokushima, Japan","institution_ids":["https://openalex.org/I922474255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071943346"],"corresponding_institution_ids":["https://openalex.org/I922474255"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":4.6213,"has_fulltext":true,"cited_by_count":51,"citation_normalized_percentile":{"value":0.95635889,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"82242","last_page":"82254"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9991999864578247,"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":0.9883000254631042,"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":0.8883614540100098},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.6595083475112915},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6583272218704224},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6005553603172302},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5906030535697937},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5768855810165405},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5358603596687317},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5304122567176819},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.47181451320648193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43816179037094116},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.42047572135925293},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.37241315841674805},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3616216480731964}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8883614540100098},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.6595083475112915},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6583272218704224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6005553603172302},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5906030535697937},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5768855810165405},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5358603596687317},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5304122567176819},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.47181451320648193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43816179037094116},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.42047572135925293},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37241315841674805},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3616216480731964},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2020.2991484","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2991484","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09082602.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c5f9ad90eb3b4ac3a84d82af27296eb7","is_oa":true,"landing_page_url":"https://doaj.org/article/c5f9ad90eb3b4ac3a84d82af27296eb7","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 82242-82254 (2020)","raw_type":"article"},{"id":"pmh:oai:irdb.nii.ac.jp:00906:0006780236","is_oa":true,"landing_page_url":"https://tokushima-u.repo.nii.ac.jp/records/2008307","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access","raw_type":"journal article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2991484","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2991484","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09082602.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3022089060.pdf","grobid_xml":"https://content.openalex.org/works/W3022089060.grobid-xml"},"referenced_works_count":68,"referenced_works":["https://openalex.org/W152728645","https://openalex.org/W943435019","https://openalex.org/W1503312748","https://openalex.org/W1526096287","https://openalex.org/W1832693441","https://openalex.org/W2054487404","https://openalex.org/W2096733369","https://openalex.org/W2097550833","https://openalex.org/W2123442489","https://openalex.org/W2128970689","https://openalex.org/W2146785422","https://openalex.org/W2148769295","https://openalex.org/W2150193144","https://openalex.org/W2156303437","https://openalex.org/W2187089797","https://openalex.org/W2251584308","https://openalex.org/W2306941105","https://openalex.org/W2400397113","https://openalex.org/W2508865106","https://openalex.org/W2561929995","https://openalex.org/W2573626026","https://openalex.org/W2610728795","https://openalex.org/W2747238065","https://openalex.org/W2749786013","https://openalex.org/W2756640946","https://openalex.org/W2757016771","https://openalex.org/W2761590056","https://openalex.org/W2803392141","https://openalex.org/W2803609229","https://openalex.org/W2889065492","https://openalex.org/W2891430112","https://openalex.org/W2896457183","https://openalex.org/W2898856000","https://openalex.org/W2904882903","https://openalex.org/W2931751229","https://openalex.org/W2955260208","https://openalex.org/W2962739339","https://openalex.org/W2963026768","https://openalex.org/W2963033987","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963550483","https://openalex.org/W2963702064","https://openalex.org/W2963744743","https://openalex.org/W2963765493","https://openalex.org/W2963775347","https://openalex.org/W2963974889","https://openalex.org/W2964331270","https://openalex.org/W2988157054","https://openalex.org/W2990149993","https://openalex.org/W4297683418","https://openalex.org/W4385245566","https://openalex.org/W6631369687","https://openalex.org/W6681675941","https://openalex.org/W6682864246","https://openalex.org/W6691697777","https://openalex.org/W6725015247","https://openalex.org/W6731868365","https://openalex.org/W6739901393","https://openalex.org/W6744519841","https://openalex.org/W6745297980","https://openalex.org/W6751425476","https://openalex.org/W6755207826","https://openalex.org/W6755520021","https://openalex.org/W6755855549","https://openalex.org/W6757078890","https://openalex.org/W6761303817","https://openalex.org/W6981062633"],"related_works":["https://openalex.org/W2529301793","https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W2619127353","https://openalex.org/W2786094008","https://openalex.org/W3131501806","https://openalex.org/W2799683370","https://openalex.org/W2807745940"],"abstract_inverted_index":{"Understanding":[0],"the":[1,9,16,43,49,52,79,103,123,125,145,154,189,195],"user's":[2],"intention":[3,53,126,157,168],"is":[4],"an":[5],"essential":[6],"task":[7,128],"for":[8,24,51],"spoken":[10],"language":[11],"understanding":[12],"(SLU)":[13],"module":[14],"in":[15,153],"dialogue":[17],"system,":[18],"which":[19],"further":[20],"illustrates":[21],"vital":[22],"information":[23],"managing":[25],"and":[26,30,71,82,182,201,214],"generating":[27,122],"future":[28],"action":[29],"response.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35,57,143],"propose":[36],"a":[37,59,69,111,117,215],"triplet":[38,98],"training":[39,50],"framework":[40],"based":[41],"on":[42,163,206],"multiclass":[44],"classification":[45],"approach":[46],"to":[47,67,89,116,148],"conduct":[48,161],"detection":[54,127,158,169],"task.":[55,159],"Precisely,":[56],"utilize":[58],"Siamese":[60,87],"neural":[61],"network":[62],"architecture":[63],"with":[64,136],"metric":[65],"learning":[66,110],"construct":[68],"robust":[70],"discriminative":[72],"utterance":[73,91,150],"feature":[74,140,151],"embedding":[75],"model.":[76],"We":[77,160],"modified":[78],"RMCNN":[80],"model":[81,85],"fine-tuned":[83],"BERT":[84],"as":[86,139],"encoders":[88],"train":[90],"triplets":[92],"from":[93,113],"different":[94],"semantic":[95],"aspects.":[96],"The":[97,185],"loss":[99],"can":[100,129,192],"effectively":[101,193],"distinguish":[102],"details":[104],"of":[105,156,167,198],"two":[106],"input":[107],"data":[108],"by":[109],"mapping":[112],"sequence":[114],"utterances":[115],"compact":[118],"Euclidean":[119],"space.":[120],"After":[121],"mapping,":[124],"be":[130],"easily":[131],"implemented":[132],"using":[133],"standard":[134],"techniques":[135],"pre-trained":[137],"embeddings":[138],"vectors.":[141],"Besides,":[142],"use":[144],"fusion":[146],"strategy":[147],"enhance":[149],"representation":[152],"downstream":[155],"experiments":[162],"several":[164],"benchmark":[165],"datasets":[166,200,209],"task:":[170],"Snips":[171],"dataset,":[172,174,181,211],"ATIS":[173],"Facebook":[175,212],"multilingual":[176],"task-oriented":[177,208],"datasets,":[178],"Daily":[179],"Dialogue":[180,219],"MRDA":[183],"dataset.":[184],"results":[186,205],"illustrate":[187],"that":[188],"proposed":[190],"method":[191],"improve":[194],"recognition":[196],"performance":[197],"these":[199],"achieves":[202],"new":[203],"state-of-the-art":[204],"single-turn":[207],"(Snips":[210],"dataset),":[213],"multi-turn":[216],"dataset":[217],"(Daily":[218],"dataset).":[220]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}