Abstrakti
The preservation of linguistic diversity has long been recognized
as a crucial, integral part of supporting our cultural heritage. Yet
many “minority” languages—those that lack official state status—are in
decline, many severely endangered. We present a prototype system aimed
at “heritage” speakers of endangered Finno-Ugric languages. Heritage
speakers are people who have heard the language used by the older generations
while they were growing up, and who possess a considerable
passive competency—well beyond the “beginner” level,—but are lacking
in active fluency.
Our system is based on natural language processing and artificial intelligence.
It assists the learners by allowing them to learn from arbitrary
texts of their choice, and by creating exercises that engage them in active
production of language—rather than in passive memorization of material.
Continuous automatic assessment helps guide the learner toward
improved fluency. We believe that providing such AI-based tools will help
bring these languages to the forefront of the modern digital age, raise
prestige, and encourage the younger generations to become involved in
reversal of language decline.
as a crucial, integral part of supporting our cultural heritage. Yet
many “minority” languages—those that lack official state status—are in
decline, many severely endangered. We present a prototype system aimed
at “heritage” speakers of endangered Finno-Ugric languages. Heritage
speakers are people who have heard the language used by the older generations
while they were growing up, and who possess a considerable
passive competency—well beyond the “beginner” level,—but are lacking
in active fluency.
Our system is based on natural language processing and artificial intelligence.
It assists the learners by allowing them to learn from arbitrary
texts of their choice, and by creating exercises that engage them in active
production of language—rather than in passive memorization of material.
Continuous automatic assessment helps guide the learner toward
improved fluency. We believe that providing such AI-based tools will help
bring these languages to the forefront of the modern digital age, raise
prestige, and encourage the younger generations to become involved in
reversal of language decline.
Alkuperäiskieli | englanti |
---|---|
Lehti | CEUR Workshop Proceedings |
Vuosikerta | 2084 |
ISSN | 1613-0073 |
Tila | Julkaistu - 2018 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
Tapahtuma | Digital humanities in the Nordic Countries DHN2018 - University of Helsinki, Helsinki, Suomi Kesto: 7 maalisk. 2018 → 9 maalisk. 2018 Konferenssinumero: 3 https://www.helsinki.fi/en/helsinki-centre-for-digital-humanities/dhn-2018 |
Tieteenalat
- 113 Tietojenkäsittely- ja informaatiotieteet
- 6121 Kielitieteet
Projektit
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LLL: Language Learning Lab
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Projekti: Tutkimusprojekti
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Revita: Language learning and AI
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Projekti: Tutkimusprojekti
Palkinnot
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Best Paper Award
Yangarber, Roman (Vastaanottaja) & Katinskaia, Anisia (Vastaanottaja), 1 toukok. 2018
Palkinto: Palkinnot ja kunnianosoitukset
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The Open Science Award
Yangarber, Roman (Vastaanottaja) & Katinskaia, Anisia (Vastaanottaja), 1 toukok. 2018
Palkinto: Palkinnot ja kunnianosoitukset