{"id":9341,"date":"2021-09-06T08:00:00","date_gmt":"2021-09-06T06:00:00","guid":{"rendered":"https:\/\/kinit.sk\/?p=9341"},"modified":"2021-09-02T10:58:20","modified_gmt":"2021-09-02T08:58:20","slug":"cross-lingual-learning","status":"publish","type":"post","link":"https:\/\/kinit.sk\/sk\/cross-lingual-learning\/","title":{"rendered":"Cross-Lingual Learning"},"content":{"rendered":"
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Mat\u00fa\u0161 Pikuliak<\/a> je n\u00e1\u0161 najnov\u0161\u00ed doktorand, ktor\u00fd ned\u00e1vno obh\u00e1jil titul PhD. Vo svojom v\u00fdskume sa venoval pr\u00e1ve met\u00f3dam, ktor\u00e9 by mali preklen\u00fa\u0165 nerovnos\u0165 medzi jazykmi a prinies\u0165 u\u017eito\u010dn\u00e9 aplik\u00e1cie \u013eudom a komunit\u00e1m, ktor\u00e9 doteraz nemali mo\u017enos\u0165 naplno vyu\u017e\u00edva\u0165 pokrok zalo\u017een\u00fd na hlbokom u\u010den\u00ed vo svojej materinskej re\u010di.<\/p>\n<\/div>\n\n

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Hlbok\u00e9 u\u010denie v posledn\u00fdch rokoch prinieslo v\u00fdrazn\u00e9 pokroky v spracovan\u00ed prirodzen\u00e9ho jazyka. Strojov\u00fd preklad, vyh\u013ead\u00e1vanie inform\u00e1ci\u00ed, automatick\u00fd prepis re\u010di do textu a in\u00e9 aplik\u00e1cie, ktor\u00e9 dnes be\u017ene pou\u017e\u00edvame, dosiahli pr\u00e1ve v\u010faka hlbok\u00e9mu u\u010deniu v\u00fdrazne zlep\u0161enie. Podobne ako v in\u00fdch dom\u00e9nach, aj tu je tento pokrok z\u00e1visl\u00fd na zozbieran\u00ed d\u00e1tov\u00fdch mno\u017e\u00edn, z ktor\u00fdch sa modely strojov\u00e9ho u\u010denia u\u010dia vykon\u00e1va\u0165 zadan\u00fa \u00falohu. Z rozli\u010dn\u00fdch historick\u00fdch, sociologick\u00fdch, technologick\u00fdch a in\u00fdch pr\u00ed\u010din s\u00fa dnes niektor\u00e9 jazyky (napr. angli\u010dtina, mandar\u00ednska \u010d\u00edn\u0161tina) ve\u013emi popul\u00e1rne, ovl\u00e1daj\u00fa ich miliardy \u013eud\u00ed a prebieha v ich spracovan\u00ed pomocou umelej inteligencie bohat\u00fd v\u00fdskum. Na druh\u00fa stranu existuj\u00fa tis\u00edce jazykov, ktor\u00e9 s\u00fa na okraji z\u00e1ujmu, ovl\u00e1da ich r\u00e1dovo menej \u013eud\u00ed, netvoria sa v nich nov\u00e9 texty v takom objeme ako v in\u00fdch jazykoch. Pou\u017eitie pr\u00edstupov umelej inteligencie je v nich \u010dastokr\u00e1t omnoho zlo\u017eitej\u0161ie a niekedy a\u017e nemo\u017en\u00e9, najm\u00e4 preto, \u017ee pre tieto jazyky neexistuj\u00fa dostato\u010dne ve\u013ek\u00e9 d\u00e1tov\u00e9 mno\u017einy. <\/p>\n<\/div>\n\n

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Mat\u00fa\u0161 je n\u00e1\u0161 najnov\u0161\u00ed doktorand, ktor\u00fd ned\u00e1vno obh\u00e1jil titul PhD. Vo svojom v\u00fdskume sa venoval pr\u00e1ve met\u00f3dam, ktor\u00e9 by mali preklen\u00fa\u0165 nerovnos\u0165 medzi jazykmi a prinies\u0165 u\u017eito\u010dn\u00e9 aplik\u00e1cie \u013eudom a komunit\u00e1m, ktor\u00e9 doteraz nemali mo\u017enos\u0165 naplno vyu\u017e\u00edva\u0165 pokrok zalo\u017een\u00fd na hlbokom u\u010den\u00ed vo svojej materinskej re\u010di. Presk\u00famal mo\u017enosti tzv. u\u010denia medzi jazykmi. Ide o paradigmu strojov\u00e9ho u\u010denia, kedy sa znalosti a d\u00e1ta vytvoren\u00e9 v jednom jazyku pren\u00e1\u0161aj\u00fa rozli\u010dn\u00fdmi technikami do in\u00fdch jazykov. Napr\u00edklad, ak sa umel\u00e1 inteligencia nau\u010d\u00ed rie\u0161i\u0165 probl\u00e9m vyh\u013ead\u00e1vania inform\u00e1cii v angli\u010dtine, snahou t\u00fdchto techn\u00edk je prenies\u0165 tieto znalosti do in\u00fdch jazykov tak, aby sme aj v nich vedeli vyh\u013ead\u00e1va\u0165.<\/p>\n<\/div>\n\n

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S\u00fa\u010das\u0165ou Mat\u00fa\u0161ovej pr\u00e1ce<\/a> bolo vykonanie jednej z v\u00f4bec prv\u00fdch preh\u013eadov\u00fdch \u0161t\u00fadi\u00ed v tejto oblasti a podarilo sa ju publikova\u0165 v \u010dasopise Expert Systems With Application<\/em> (v Google Scholar je to \u010dasopis zo sekcie Umel\u00e1 Inteligencia s najvy\u0161\u0161\u00edm h-indexom). Z\u00e1kladom tohto preh\u013eadu bolo zmapovanie doteraz pou\u017eit\u00fdch pr\u00edstupov k u\u010deniu medzi jazykmi, kde sa zanalyzovalo vy\u0161e 170 doteraz existuj\u00facich pr\u00e1c. D\u00f4kladn\u00e1 anal\u00fdza pou\u017eit\u00fdch jazykov, datasetov, \u00faloh a pr\u00edstupov poskytla poh\u013ead na stav oblasti, ak\u00fd doteraz ch\u00fdbal. Uk\u00e1zalo sa, \u017ee u\u010denie s prenosom medzi jazykmi je ve\u013emi v\u0161estrann\u00e1 technol\u00f3gia, pou\u017eite\u013en\u00e1 na prakticky \u013eubovo\u013en\u00fa \u00falohu spracovania prirodzen\u00e9ho jazyka. Ale uk\u00e1zala sa aj dominancia ve\u013ek\u00fdch eur\u00f3pskych jazykov pri experiment\u00e1lnej pr\u00e1ci. Aj napriek tomu, \u017ee jedn\u00fdm z hlavn\u00fdch mot\u00edvov u\u010denia s prenosom medzi jazykmi je pom\u00f4c\u0165 jazykom s nedostatkom d\u00e1t, experiment\u00e1lne overenie sa \u010dastokr\u00e1t vykon\u00e1va pr\u00e1ve na jazykoch, ktor\u00e9 tak\u00fato pomoc mo\u017eno ani a\u017e tak nepotrebuj\u00fa. Mat\u00fa\u0161 taktie\u017e prispel k teoretick\u00e9mu porozumeniu tak\u00e9hoto u\u010denia, ke\u010f ho form\u00e1lne zadefinoval a navrhol rozdelenie existuj\u00facich pr\u00edstupov pod\u013ea druhu znalosti, ktor\u00e1 sl\u00fa\u017ei na komunik\u00e1ciu medzi r\u00f4znymi jazykmi. Takto sa medzi jazykmi m\u00f4\u017eu \u0161\u00edri\u0165 anot\u00e1cie vzoriek, \u010drty vzoriek, hodnoty parametrov a vn\u00fatorn\u00e9 reprezent\u00e1cie modelov.<\/p>\n<\/div>\n\n

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Na z\u00e1klade tejto anal\u00fdzy stavu oblasti sa potom Mat\u00fa\u0161 v pr\u00e1ci venoval kombin\u00e1cii u\u010denia s prenosom medzi jazykmi a medzi \u00falohami. Zvykom bolo \u0161tudova\u0165 tieto dva pr\u00edstupy oddelene. Tu sa Mat\u00fa\u0161 odhodlal skombinova\u0165 ich a presk\u00fama\u0165 mo\u017enosti a limit\u00e1cie tak\u00e9hoto spojenia. Experimenty prebiehali s tzv. mrie\u017ekou modelov. Ka\u017ed\u00fd model mal zadefinovan\u00fa konkr\u00e9tnu \u00falohu v konkr\u00e9tnom jazyku a spolu pokr\u00fdvali v\u0161etky mo\u017en\u00e9 kombin\u00e1cie vybran\u00fdch 4 \u00faloh a 4 jazykov. Modely v takejto mrie\u017eke sa u\u010dili rie\u0161i\u0165 \u00falohu na z\u00e1klade svojich d\u00e1t. Z\u00e1rove\u0148 sa v\u0161ak u\u010dili aj od seba navz\u00e1jom v\u010faka mechanizmu, ktor\u00fd sa naz\u00fdva zdie\u013eanie parametrov. V\u00fdsledkom experimentov boli objavy v tom, ako spr\u00e1vne kombinova\u0165 d\u00e1ta z r\u00f4znych jazykov tak, aby sme maximalizovali v\u00fdkon modelov v menej sk\u00faman\u00fdch jazykoch.<\/p>\n<\/div>\n\n

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Na obr\u00e1zku je zobrazen\u00e1 mrie\u017eka 4 neur\u00f3nov\u00fdch modelov. Ka\u017ed\u00fd model rie\u0161i ur\u010dit\u00fa \u00falohu v ur\u010ditom jazyku. Farebn\u00e1 sch\u00e9ma zn\u00e1zor\u0148uje tie vrstvy, ktor\u00e9 medzi sebou modely zdie\u013eaj\u00fa. Ke\u010f\u017ee s\u00fa v\u0161etky modely poprep\u00e1jan\u00e9, vedia spolu komunikova\u0165 a vymie\u0148a\u0165 si znalosti.<\/em><\/em><\/figcaption><\/figure><\/div>\n<\/div>\n\n
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Vykonan\u00fd v\u00fdskum m\u00e1 potenci\u00e1l by\u0165 aplikovan\u00fd v praxi pri v\u00fdvoji met\u00f3d a modelov v \u201cchudobn\u00fdch\u201d jazykoch, kam v niektor\u00fdch pr\u00edpadoch patr\u00ed aj sloven\u010dina. Prenosom znalost\u00ed z bohat\u00fdch jazykov pomocou navrhnut\u00fdch met\u00f3d dok\u00e1\u017eeme zlep\u0161i\u0165 v\u00fdsledky pr\u00e1ve pre tieto jazyky. Tieto technol\u00f3gie u\u017e aj dnes pom\u00e1haj\u00fa \u013eu\u010fom, verejne strojov\u00e9 preklada\u010de (napr. Google Translate) u\u017e tak\u00e9to techniky pou\u017e\u00edvaj\u00fa a preklady do a z menej roz\u0161\u00edren\u00fdch jazykov s\u00fa \u010doraz lep\u0161ie pr\u00e1ve v\u010faka prenosu znalost\u00ed. Toto je \u010fal\u0161\u00edm krokom v pokra\u010duj\u00facej demokratiz\u00e1cii umelej inteligencie a informa\u010dn\u00fdch technol\u00f3gi\u00ed vo v\u0161eobecnosti. Pr\u00e1ca s viacer\u00fdmi jazykmi sa navy\u0161e ukazuje ako zauj\u00edmav\u00e1 v\u00fdzva aj pre \u010fal\u0161ie aktivity KInIT-u, kde dok\u00e1\u017eeme podobn\u00e9 techniky pou\u017ei\u0165 aj v na\u0161ich existuj\u00facich projektoch zameran\u00fdch napr. na boj proti dezinform\u00e1ciam alebo v r\u00f4znych v\u00fdskumn\u00fdch projektoch na\u0161ich partnerov.<\/p>\n<\/div>\n\n

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V\u00fdber z Mat\u00fa\u0161ov\u00fdch<\/a><\/strong> publik\u00e1ci\u00ed:<\/strong><\/h3>\n<\/div>\n\n
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1. Pikuliak, M., \u0160imko, M., & Bielikova, M. (2021). Cross-lingual learning for text processing: A survey. Expert Systems with Applications<\/a>, 165, 113765.<\/p>\n<\/div>\n\n

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2. Pikuliak, M., Simko, M., & Bielikova, M. (2019, January). Towards combining multitask and multilingual learning<\/a>. In International Conference on Current Trends in Theory and Practice of Informatics (pp. 435-446). Springer, Cham.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

Mat\u00fa\u0161 Pikuliak je n\u00e1\u0161 najnov\u0161\u00ed doktorand, ktor\u00fd ned\u00e1vno obh\u00e1jil titul PhD. Vo svojom v\u00fdskume sa venoval pr\u00e1ve met\u00f3dam, ktor\u00e9 by mali preklen\u00fa\u0165 nerovnos\u0165 medzi jazykmi a prinies\u0165 u\u017eito\u010dn\u00e9 aplik\u00e1cie \u013eudom…<\/p>\n","protected":false},"author":4,"featured_media":6627,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[76,82,88],"tags":[187],"class_list":["post-9341","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-natural-language-processing-sk","category-2021-sk","category-pop-science-sk","tag-nlp-sk"],"acf":[],"yoast_head":"\nCross-Lingual Learning - KInIT<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/kinit.sk\/sk\/cross-lingual-learning\/\" \/>\n<meta property=\"og:locale\" content=\"sk_SK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cross-Lingual Learning - KInIT\" \/>\n<meta property=\"og:description\" content=\"Mat\u00fa\u0161 Pikuliak je n\u00e1\u0161 najnov\u0161\u00ed doktorand, ktor\u00fd ned\u00e1vno obh\u00e1jil titul PhD. 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