{"id":"https://openalex.org/W2899310564","doi":"https://doi.org/10.1109/mlsp.2018.8517007","title":"Supportive Attention in End-To-End Memory Networks","display_name":"Supportive Attention in End-To-End Memory Networks","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2899310564","doi":"https://doi.org/10.1109/mlsp.2018.8517007","mag":"2899310564"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2018.8517007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2018.8517007","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5061908942","display_name":"Jen\u2010Tzung Chien","orcid":"https://orcid.org/0000-0003-3466-8941"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jen-Tzung Chien","raw_affiliation_strings":["Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084491869","display_name":"Ting-an Lin","orcid":"https://orcid.org/0000-0002-3519-6221"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ting-An Lin","raw_affiliation_strings":["Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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":0.9993000030517578,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9979000091552734,"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/end-to-end-principle","display_name":"End-to-end principle","score":0.7877364158630371},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7682920694351196},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7482346296310425},{"id":"https://openalex.org/keywords/dialog-box","display_name":"Dialog box","score":0.6754834651947021},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6736834645271301},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6167958378791809},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6161374449729919},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5808053612709045},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5329853892326355},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.492559015750885},{"id":"https://openalex.org/keywords/sequence-learning","display_name":"Sequence learning","score":0.4570288062095642},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.44966474175453186},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.4480137526988983},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.4334031641483307},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35664883255958557}],"concepts":[{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.7877364158630371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7682920694351196},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7482346296310425},{"id":"https://openalex.org/C173853756","wikidata":"https://www.wikidata.org/wiki/Q86915","display_name":"Dialog box","level":2,"score":0.6754834651947021},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6736834645271301},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6167958378791809},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6161374449729919},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5808053612709045},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5329853892326355},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.492559015750885},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.4570288062095642},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.44966474175453186},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.4480137526988983},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.4334031641483307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35664883255958557},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2018.8517007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2018.8517007","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.699999988079071,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W854541894","https://openalex.org/W1525961042","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2242047860","https://openalex.org/W2293453011","https://openalex.org/W2747695703","https://openalex.org/W2771697229","https://openalex.org/W2891462019","https://openalex.org/W2950527759","https://openalex.org/W2951008357","https://openalex.org/W2963448850","https://openalex.org/W2963579811","https://openalex.org/W2964210218","https://openalex.org/W2964308564","https://openalex.org/W4295249402","https://openalex.org/W4303633609","https://openalex.org/W6623517193","https://openalex.org/W6631399359","https://openalex.org/W6638318767","https://openalex.org/W6679434410","https://openalex.org/W6679436768","https://openalex.org/W6684821475","https://openalex.org/W6697449767","https://openalex.org/W6712785310","https://openalex.org/W6732742072","https://openalex.org/W6745960141"],"related_works":["https://openalex.org/W2098987383","https://openalex.org/W2417260800","https://openalex.org/W1596203174","https://openalex.org/W2117933979","https://openalex.org/W2283130723","https://openalex.org/W103938586","https://openalex.org/W2104718772","https://openalex.org/W4233992201","https://openalex.org/W2939790162","https://openalex.org/W2975183808"],"abstract_inverted_index":{"Attention":[0],"over":[1],"natural":[2],"language":[3],"aims":[4],"to":[5,19,75,125],"spotlight":[6],"on":[7],"the":[8,38,42,48,55,66,71,89,98,104,122,127,143],"meaningful":[9,67],"region":[10,68],"with":[11],"representative":[12],"keywords":[13],"which":[14,32,64,80,120],"can":[15],"extract":[16],"desirable":[17],"meanings":[18],"accomplish":[20],"a":[21,29,61,111,117],"task":[22,43],"of":[23,44,132],"interest.":[24],"The":[25,130],"attention":[26,113],"parameter":[27],"is":[28,108,114,135],"latent":[30],"variable":[31],"was":[33],"indirectly":[34],"estimated":[35],"by":[36,69,141],"minimizing":[37,70,88],"classification":[39,49,90],"loss.":[40],"For":[41],"question":[45,106],"answering":[46,107],"(QA),":[47],"loss":[50,73],"may":[51],"not":[52],"sufficiently":[53,136],"reflect":[54],"target":[56],"answer.":[57],"This":[58],"paper":[59],"proposes":[60],"direct":[62],"solution":[63],"attends":[65],"reconstruction":[72,92],"due":[74],"auxiliary":[76],"or":[77],"supporting":[78,123],"data":[79],"are":[81,94],"available":[82],"in":[83],"different":[84],"scenarios.":[85],"In":[86],"particular,":[87],"and":[91,146],"losses":[93],"carried":[95],"out":[96],"under":[97],"end-to-end":[99],"memory":[100],"network":[101],"so":[102],"that":[103],"memory-augmented":[105],"realized.":[109],"Such":[110],"supportive":[112],"implemented":[115],"as":[116],"sequence-to-sequence":[118],"model":[119],"reconstructs":[121],"sentence":[124],"assure":[126],"translation":[128],"invariance.":[129],"merit":[131],"this":[133],"method":[134],"demonstrated":[137],"for":[138],"sequential":[139],"learning":[140],"using":[142],"bAbI":[144],"QA":[145],"dialog":[147],"tasks.":[148]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}