Tools for supporting language learning for Sakha
Sardana Ivanova, Anisia Katinskaia, Roman Yangarber University of Helsinki [email protected]
Abstract words, has many borrowings from the surrounding This paper presents an overview of linguis- Mongolic and Tungusic languages, numerous loan- tic resources available for the Sakha lan- words from Russian, as well as words of unknown guage, and presents new tools for support- origin. Sakha makes extensive use of post-positions, ing language learning for Sakha. The essen- which indicate syntactic relations and govern the tial resources include a morphological an- grammatical case of nominals, (Forsyth, 1994). alyzer, digital dictionaries, and corpora of In the digital sphere, Sakha can be considered a Sakha texts. We extended an earlier version low-resource language. We report on our project of the morphological analyzer/transducer, to provide learning support for Sakha. Building built on the Apertium finite-state platform. on pre-existing digital resources, we aim to provide The analyzer currently has an adequate level a learning platform for students (including adults) of coverage, between 86% and 89% on two who are interested in strengthening their linguistic Sakha corpora. Based on these resources, competency in Sakha. we implement a language-learning environ- The paper is structured as follows. Section 2 de- ment for Sakha in the Revita computer- scribes distinctive properties of the Sakha language assisted language learning (CALL) plat- and motivates the need for language-learning sup- form. Revita is a freely available online lan- port by reviewing the social environment of the lan- guage learning platform for learners beyond guage. Section 3.1 presents an overview of previous the beginner level. We describe the tools for work Sakha; Section 3.2 describes the Revita plat- Sakha currently integrated into the Revita form for language learning. Section 4 describes the platform. To our knowledge, at present this instruments we integrate to support language learn- is the first large-scale project undertaken to ing for Sakha. In Section 5 we discuss initial results support intermediate-advanced learners of obtained with the tools. Sections 6 concludes with a minority Siberian language. pointers for future work. 1 Introduction 2 Sakha language The Sakha language, also known by its exonym Sakha is the national language of the Sakha people, Yakut, is the language of an ethnic community, who which, along with Russian, is one of the official lan- mainly inhabit the Republic of Sakha in the Far guages of the Republic of Sakha (Yakutia), (Yart- East of Siberia, Russian Federation. According to seva, 1990). The Sakha language differs signifi- the 2010 census, Sakha is the native language of cantly from other Turkic languages by the presence 450,140 people, and is considered vulnerable due of a layer of vocabulary of unclear (possibly Paleo- to its limited usage. Children do not use Sakha in Asiatic) origin, (Kharitonov, 1987). There are also a all aspects of their life; they speak Sakha at home large number of words of Mongolic origin related to with family, but do not use it in school and socially. ancient borrowings, as well as late borrowings from Sakha belongs to the Northern group of the the Russian language, (Tenishev, 1997). Siberian branch of the Turkic language family, and is agglutinative, as are all Turkic languages, (Ubry- 2.1 Distinctive features atova, 1982). It has complex, four-way vowel har- Vowels in Sakha follow complex vowel harmony mony, and a basic Subject-Object-Verb word or- rules. The features of the vowels within a word must der. The lexicon of Sakha consists of native Turkic agree in a strictly defined fashion. First, palatal- velar harmony of vowels is observed in Sakha • remote-past: strictly sequentially and does not admit exceptions. “үлэлээбитим” [¨ulel¯ebitim] If the first syllable contains a front vowel, then the “I worked (long ago)”; vowels in all subsequent syllables in the word must • past perfect: be front. Otherwise, if the first syllable contains a “үлэлээбиппин” [¨ulel¯ebippin] back vowel, then the vowels in all subsequent syl- “In fact, I worked”; lables must be back. Second, labial vowel harmony • episodic past: requires that the sequence of vowels agree according “үлэлээбиттээхпин” [¨ulel¯ebitt¯eXpin] to the degree of roundedness within adjacent sylla- “I used to work on occasion”; bles, (Sleptsov, 2018). For example: • past imperfect: • back+unrounded: “аҕа / аҕалардыын” “үлэлиирим” [¨ulel¯irim] [aGa / aGalard¯In] “I worked in the past for some time”; “father/with fathers” • plusquamperfect: • back+rounded: “оҕо / оҕолордуун” “үлэлээбит этим” [¨ulel¯ebit etim] [oGo / oGolord¯un] “I had worked prior to that”; “child / with children” • episodic plusquamperfect: • front+unrounded: “эбэ / эбэлэрдиин” “үлэлээбиттээх этим” [¨ulel¯ebitt¯eXetim] [ebe / ebelerd¯in] “Long ago, I used to work”. “grandmother / with grandmothers” The total number of tense forms exceeds 20. • front+rounded: “бөрө / бөрөлөрдүүн” One of the particularities of nouns is when paired [b¨or¨o/ b¨or¨ol¨ord¨un¯ ] nouns are marked with possessiveness, both compo- “wolf / with wolves” nents of the compound noun change in parallel, as the word is inflected: Thus, the vowels in the suffixes “-лар-” [lar], in- dicating the plural, and “-дыын” [d¯In], indicating • “баай-дуол” [b¯ajduol] “wealth”: comitative case, undergo 4-way mutation according “баайа-дуола” [b¯aja duola] to vowel harmony. (3rd person possessive, nominative case), In Sakha, the verb is the central part of “баайын-дуолун” [b¯ajIn duolun] speech, (Dyachkovsky et al., 2018). Some verbs can (3rd person singular possessive, accusative) have multiple affixes (as in most Turkic languages), • “сурук-бичик” [suruk biˇcik] “writing”: which can correspond to an entire clause or sentence “сурукта-бичиктэ” [surukta biˇcikte] in other languages, such as Russian. Sakha has no (partitive case), infinitive form for verbs, therefore a predicate that “суругу-бичиги” [surugu biˇcigi] (in other languages) would include an infinitive is (accusative case) conveyed by various indirect means, for example: 2.2 Socio-linguistic environment • “суруйан бүтэрдэ”: [surujan b¨uterde] According to (Vasilieva et al., 2013), since 1990, “he finished writing” the percentage of ethnic Sakha has grown, reach- (literally: “he wrote, finished”); ing 45% of the total population in the Republic of • “сатаан ыллыыр”: [sat¯anIll¯Ir] Sakha. Ethnic Sakha together with other indigenous “he can sing” peoples of the North Siberia and the Far East com- (literally: “he knows how, sings”); prise over 50% of the total population. • “бобуоххун син”: [bobuoXXun sin] Vasilieva et al. (2013) has conducted surveys, “you can forbid” which show a direct dependence of the level of lin- (literally: “you can, let’s forbid”). guistic proficiency on the language of instruction at Sakha is characterized by an exceptional variety school. A fluent level of proficiency is achieved by: of verbal tenses. In particular, according to (Kork- • respondents who had schooling in the Sakha ina, 1970), 8 past forms are distinguished: language (34.5%) • proximal-past: • respondents who had studied in schools, where “үлэлээтим” [¨ulel¯etim] subjects were taught in Russian and partly in “I worked (recently)”; Sakha (27.4%). Only 17.9% of respondents who had studied in the language in a number of ways, and several Russian are fluent in Sakha. Respondents who projects are being undertaken to support Sakha. We speak Sakha poorly, or do not speak at all, graduated briefly mention some of them here. from Russian-speaking schools. Thus, as expected, The digital bilingual dictionary SakhaTyla.Ru1 linguistic skills and abilities in Sakha are poorer for currently offers over 20,000 items from Sakha to those who had studied in Russian. Russian, over 35,000 items from Russian to Sakha, In work life, the Russian language is dominant about 2,000 items from Sakha to English, and about for all age groups. In the two youngest age groups 1,000 items from English to Sakha. In addition to (16–25 and 26–35 years old), the use of Russian is translations, this dictionary also contains examples growing, approaching 50%. This is due to the re- of usage, including idiomatic usage, for every item, quirements of formal communication, terminolog- which constitutes a base of lexical data, and can ical dependence, ethnically mixed composition of be highly useful for language learning and teaching. professional teams. On the other hand, after the The base of examples from this dictionary is cur- completion of active professional life, the return to rently not utilized in our learning platform. an increased usage of the original ethnic language is Leontiev (2015) has compiled a newspaper cor- common, (Vasilieva et al., 2013). pus of Sakha containing over 12 million tokens. In Yakutsk—the capital and the largest city of The Sakha Wikipedia contains over 12,000 articles, the Sakha Republic—one in three Sakha children which makes up a corpus of over 2 million tokens.2 lack the opportunity to study in their native lan- A Sakha course on the educational platform guage. This is a violation of the right to study Memrise offers a vocabulary of about 3100 words.3 in one’s native language. The number of schools Audio materials: Common Voice is a platform which offer teaching in Sakha in Yakutsk in 2002– for crowdsourcing open-source audio datasets.4 At 2003 was 16, and dropped to 15 by 2003–2004. present, it offers just under 2.5 hours validated The number of schools where Sakha is studied as voice recordings in Sakha. By comparison, English a subject decreased from 22 (in 2002–2003) to 16 has almost 850 hours of audio content on the plat- (in 2003–2004). The number of Sakha language form, and Russian has 50 hours. learners decreased from 6,377 to 2,902, (Vasilieva In summary, few linguistic resources exists for et al., 2013). According to the statistical report Sakha. of the Ministry of Education of the Republic of Sakha in 2006–2007, the cities of the Republic had 3.2 Revita language learning platform 147 schools with Russian language of instruction Revita is an e-learning platform, which uses (61,055 children), 4 educational institutions with methods from computer-assisted language learn- non-Russian languages of instruction (1014 chil- ing (CALL) and intelligent tutoring systems (ITS).5 dren), 29 institutions with a mix of Russian and non- The platform provides a language-independent Russian languages of instruction (18,094 children). foundation for language learning, which can be In 11 schools (serving 1,262 students) non-Russian adapted to support new languages, by adding languages are offered as optional subjects of study. language-specific resources, without modifying the Vasilieva et al. (2013) indicate that this situa- core system. The platform is used for language tion concerning the language of instruction of eth- teaching and learning at several universities in Eu- nic Sakha pupils has a direct correlation with other rope and Asia. serious problems in terms of linguistic competency The goal of the system is to provide tools for in Sakha and vitality of Sakha—acculturation and language learning, (Katinskaia et al., 2018), and assimilation of urban youth, which will leads to lin- to support endangered languages, (Katinskaia and guistic conformism due to the lack of sufficient so- Yangarber, 2018; Yangarber, 2018). The system cial opportunities for using the language. focuses on stimulating the student to actively pro- duce language, rather than passively absorb exam- 3 Prior work 1www.sakhatyla.ru 3.1 Sakha language resources 2sah.wikipedia.org 3 Despite the current advances in digitization, digital www.memrise.com/course/153579/sakha-tylyn- leksikata-sakha-tyla-iakutskii/ resources for the Sakha language are severely lack- 4voice.mozilla.org/en/about ing. The creation of digital tools would strengthen 5revita.cs.helsinki.fi ples of language use or grammatical rules. The sys- • атын [atIn]
The system creates cloze quizzes from inflected The previous snippets—above the current parts of speech, i.e., nouns, verbs, etc. For example, snippet—show correctly answered questions— the first sentence in the snippet in Figure 1is coloured in green—and incorrectly answered “Туйаара уонна Айаал оонньуулларын questions in blue. сөбүлүүллэр .” The choice of candidates for exercises depends [tuj¯arauonna aj¯al¯onnj ¯ullarIns¨ob¨ul¨uller]¯ on the user model—based on the history of the “Tuyaara and Ayaal like to play.” user’s previous answers. The system computes probabilities (weights) for potential candidates in The current snippet of the story appears over a the snippet. Exercises receive a lower probability grey background, and contains cloze and multiple- if the student had mostly answered them correctly choice exercises. The system created a cloze exer- or mostly incorrectly in earlier sessions—since it cise, showing to the user only the base form (lemma) means that they are too easy or too difficult for the “сөбүлээ” [s¨ob¨ul¯e] (“to like”) of the surface form learner at present. “сөбүлүүллэр” [s¨ob¨ul¨uller]¯ (“they like”): In the crossword mode, a crossword is built based “Туйаара уонна Айаал оонньуулларын on the text. Exercises for the crossword are selected сөбүлээ ...” randomly, and according to the same principles as [tuj¯arauonna aj¯al¯onnj ¯ullarIns¨ob¨ul¯e] in practice mode. The user can receive the translation of an unfa- From the verb lemma the learner should guess miliar word by clicking on it. The box on the left in which form of the hidden word fits the context best. Figure 1 shows a dictionary entry for a token clicked Multiple-choice quizzes are constructed also by the user—”дьиэ” [ˇZie] (“house”). The learner from non-inflected parts of speech (adverbs, post- can request a translation of an unfamiliar word in positions, etc.) Tokens of similar part of speech are all practice modes. Translations are looked up in presented to the learner as “distractors”—incorrect the SakhaTyla.Ru digital dictionary. The system answer options. Figure 1 shows a multiple-choice records the words for which translations were re- quiz for the token “куруук” [kur¯uk] (“always”) with quested into the user’s own set of flashcards. In the other adverbs serving as distractors. flashcard mode the user can practice vocabulary, us- Listening exercises (optional) are generated from ing timed repetition algorithms. tokens in the story—the words are spoken by a speech synthesizer and the learner must enter the Stories for learning can be found on newspaper word that was pronounced. Currently, listening ex- websites, such as edersaas.ru, kyym.ru, etc., or from 6 ercises are not available for Sakha; we plan to in- the Sakha Wikisource. corporate them into the system when text-to-speech (TTS) synthesis for Sakha becomes available. 6https://sah.wikisource.org/ Figure 2: Example of a chunk
4.3 Chunking for exercises Corpus Tokens % Coverage Revita allows the language expert to customize the Original analyzer: system for a new language by explicitly specify- Wikipedia 2015 1,020,000 73.02 ing rules for syntactic government and agreement. Kyym (newspaper) 1,040,000 71.36 The system performs shallow parsing (“chunking”) Improved analyzer: based on these rules, and uses the chunks when cre- Wikipedia 2019 2,195,565 89.28 ating exercises, discussed in the previous section. Newspapers 16,436,999 86.41 Next we discuss how these rules can work for Sakha. Table 2: Coverage of the morphological analyzer A government rule, Rg, may state: (сылтаан, PostP) → [Ablative] which means that the post-position lemma The boxes contain the cloze quizzes—exercises for сылтаан, [sIlt¯an], (meaning “because-of”), the user. The system provides hints for each cloze. governs the ablative case of its preceding noun (or First, inside the box it shows the lemma of the noun phrase). word. Further, each phrase circled in red forms a A simple but quite general agreement rule, Ra, chunk/unit—based on the government and agree- may state: ment rules, such as Rg and Ra, above. Thus, [ {pos: Noun, case: $X } the post-position “сылтаан” [sIlt¯an] (“because of”), + {pos: PostP, gov_case: $X } ] which governs the ablative case, links it to its pre- Rule Ra consists of two elements/tokens, and de- ceding noun “ардах” [ardaX] (“rain”) to hint to the scribes case agreement. If a token with noun POS user that the noun’s surface form should be in the is followed by a token with postposition POS, they ablative. will form a unit (phrase) if the free variable $X, in- dicating the value of the case feature of the noun 5 Discussion and the case that the postposition governs, has the 5.1 Analyzer coverage same value for both tokens. Using these two rules, the systems will match all Table 2 shows the coverage of the improved mor- corresponding constructions in text. For example: phological analyzer, as compared to the original one. Coverage of the original analyzer was mea- “... ардахтан сылтаан ... ” sured on the Wikipedia corpus (dump from 2015) [ardaXtan sIlt¯an] and the “Kyym” Sakha newspaper. Rain.Noun.ABL because_of.Post-position The improved analyzer was tested on a Wikipedia “... because of rain ...” dump from 2019, and the large newspaper corpus The Revita language learning system uses these compiled by Leontiev (2015). Currently, the cov- chunk rules to construct exercises. For example, the erage on Wikipedia is about nine out of ten tokens, exercise based on the second sentence in Figure 2: which is higher, as expected, since the analyzer was developed based on a frequency list from this cor- “Ол гын баран бэҕэһээ ардахт сылтаан pus. олус тымныы этэ!” [ol gIn baran beGeh¯e ardaX sIlt¯anolus 5.2 Learner engagement tImn¯Iete] We have presented the language learning platform “But it was very cold yesterday because of the to language experts and lecturers at the Department rain!” of the Sakha language, at the North-Eastern Fed- eral University in Yakutsk, Russian Federation. The • “билбэт киһитэ” experts confirm that the language learning system [bilbet kihite] can be a promising tool for enhancing instruction “man whom s/he doesn’t know” in Sakha. We also plan to introduce the learning (literally: “her man, whom ...”). system to Sakha learners in cooperation with Yaku- We often find examples of using an impersonal tia.Team, the organization for promoting Sakha lan- form of a noun in place of a possessive form of a guage and culture.7 noun and vice versa, as in the following examples: Releasing the system for use by language learners will yield mutual benefits for the learners as well as • Example of using an impersonal noun instead for the researchers. The learners receive a training of a possessive noun: platform which helps them improve their linguistic 1a. Form which was used in a story: competency. From the interaction of the learners “ сааны ылан сүгэр” with the platform, the researchers receive valuable [s¯anIIlan s¨uger] educational data, for modeling the process of learn- (“he hung a gun on his shoulder”) ing Sakha, the patterns of mistakes that the learners 1b. The learner’s input: make over time, and insights into how the learning “ саатын ылан сүгэр” system can be improved on the basis of the collected [s¯atInIlan s¨uger] data. (“he hung his gun on his shoulder”) 5.3 Multiple admissibility • Example of using a possessive form instead of Multiple Admissibility (MA) in language learning an impersonal form: occurs when more than one surface form of a given 2a. Form which was used in a story: lemma fits syntactically and semantically within a “моонньун уһатан уутун көрөр” given context. MA implies that multiple alternative [m¯onnjun uhatan ¯utunk¨or¨or] answers are “correct” for the given context, not only (“craning his neck, he looks at (his) wa- the word that the author chose to use in the story. ter”) From the perspective of CALL and ITS (intelligent tutoring systems), MA forms a complex challenge, 2b. The learner’s input: discussed in current research, (Katinskaia et al., “моонньун уһатан ууну көрөр” 2019). [m¯onnjun uhatan ¯unuk¨or¨or] The Sakha language presents a particularly rich (“craning his neck, he looks at the water”) source of scenarios for multiple-admissible an- Secondly, Sakha has a highly developed system swers. Due to the agglutinative morphology of of verbal aspects. Aspect in Sakha can be expressed Sakha, the learner can add affixes to a word, by various affixes or analytically. Aspect is one of which carry additional information or connotations the most commonly used grammatical categories in to slightly alter the meaning of the word. We briefly Sakha, which allows statements in the language to discuss several such scenarios. be expressive and precise, (Ubryatova, 1982). Eight The category of possessiveness—possessive af- forms are used to designate actions that have oc- fixes on nominals—is one of the fundamental cate- curred prior to the present time. gories of Sakha grammar. Possessive forms are very As a result of this choice, it may be quite difficult common, and the scope of their usage is far wider to decide which form best fits the context, given only than merely indicating possession in the strict sense; the base form of the word. For instance, it can be possessive affixes express a wide range of logical re- difficult to distinguish the “first past” perfect tense lations and connections between objects, which are and recent past tense, because the results of both often not related directly to the strict notion of pos- actions are connected with a present moment. session, (Ubryatova, 1982). For example: The verb form which was used in a story: first • “сылдьыбатах сирим” past perfect tense, indicative mood, third person, [sIlˇZIbataXsirim] singular: “a place where I have not been” “Эһэм сиргэ сылдьан өрүү түргэн (literally: “my place, where ...”) хаамыылаах, оттон бу сырыыга өссө 7https://www.yakutia.team чэпчэкитик үктэнэргэ дылы буолбут .” [ehem sirge sIlˇZan ¨or¨ut¨urgenX¯am¯Il¯aX,¯ “ангинанан ыалдьыбыт” otton bu sIr¯Iga¨oss¨oˇcepˇcekitik¨uktenerge [anginanan IalˇZIbIt] dIlI buolbut ] tonsillitis.Noun.INS contract.Verb.2SG.PAST “Grandfather usually walks fast in the field, but (“s|he contracted tonsillitis”). this time he also seems to step lighter” (meaning, The verb “ыарый” [IarIj] (“to contract”) gov- the action is inferred from its result). erns the instrumental case of the noun “ангина” The learner’s input was a verb form in the recent [angina] (“tonsillitis”). past tense, indicative mood, third person, singular: Currently, the system employs chunking (shallow “Эһэм сиргэ сылдьан өрүү түргэн parsing) to track instances of syntactic government. хаамыылаах, оттон бу сырыыга өссө The system can track more complex and longer- чэпчэкитик үктэнэргэ дылы буолла .” range government with the help of deep parsing. [ehem sirge sIlˇZan ¨or¨ut¨urgenX¯am¯Il¯aX,¯ Once a parser for Sakha becomes available, it will otton bu sIr¯Iga¨oss¨oˇcepˇcekitik¨uktenerge enable Revita to provide richer feedback to the dIlI buolla ] learner. “Grandfather usually walks fast in the field, The Sakha analyzer needs further improvement but this time he also steps lighter” (meaning, to reach higher coverage. The main work to be the action is observed by the speaker). done is extending the lexicon. While a good level of These and many other examples show that the coverage has been achieved with only 9,313 stems, task of generating exercises automatically for Sakha production-level morphological analyzers have at is far from trivial and requires much research due to least tens of thousands of stems—more typically, extensive multiple admissibility. hundreds of thousands. Once good coverage has been achieved for the morphological analyzer, the 6 Conclusion and future work next step is to build models for morphological and syntactic disambiguation. This paper offers an overview of the resources avail- As more advanced tools for Sakha become avail- able for Sakha, and describes our work on creating able, they will be incorporated into Revita, to pro- tools to support language learning for Sakha. Our vide richer functionality: surveys of available resources demonstrate that they are severely lacking. • Parsers, e.g., dependency parsers, such as We present the following tools and resources, based on the constraint-grammar formalism which we combine to create a system to support lan- (commonly used in Apertium), or statistical or guage learning for Sakha: neural-network based parsers. • morphological analyzer, built on the Apertium • Difficulty models—to predict the difficulty of platform, a story for the user, and to assess the learner’s level of competency—based on how well a • language learning system, built on the Revita learner handles easy vs. difficult stories. platform, • Disambiguation models—to disambiguate am- • bilingual Sakha dictionaries, biguous tokens in text. • several Sakha corpora. • Text-to-speech—to provide listening exercises The morphological analyzer is an essential com- based on text. ponent in any natural language processing (NLP) • Speech-to-text—to provide speaking exercises system, without which little can be done to provide (not yet available). computational tools for the language. The functionality of the language learning sys- Acknowledgements tem is under development. For larger languages many more linguistic resources and tools are avail- This research was supported in part by the “Fin- able than for Sakha. For example, currently, the UgRevita” Project, Academy of Finland, Grant Sakha system has only noun–postposition govern- 267097, and EDUFI Fellowship TM-18-10846. ment rules. We plan to implement additional We are very grateful to Javad Nouri, Kim Salmi, shallow-parsing rules to provide intelligent error Max Koppatz, José María Hoya Quecedo, and Jue feedback to the learners. For example, verb– Hou for their contributions. complement government: References Petr Sleptsov. 2018. Саха тылын быһаарыылаах улахан тылдьыта: Большой толковый словарь Katja Bang. 2015. Language situation in the republic якутского языка. (Large explanatory dictionary of of Sakha (Yakutia). Master’s thesis, University of the Yakut language: in 15 volumes). Novosibirsk, Turku, School of Languages and Translation Studies. Nauka. Nikolay Dyachkovsky, Petr Sleptsov, K Fedorov, and Edhem Tenishev. 1997. Языки мира: Тюркские языки M Cherosov. 2018. Поговорим по-якутски. Само- (Languages of the World: Turkic languages), vol- учитель языка саха (Let’s speak Sakha. Sakha lan- ume 2. Moscow: Indrik. guage tutorial). Yakutsk: Bichik. Elizaveta Ubryatova. 1982. Грамматика современ- Mikel L Forcada, Mireia Ginestí-Rosell, Jacob Nord- ного якутского литературного языка (Grammar falk, Jim O’Regan, Sergio Ortiz-Rojas, Juan An- of the modern Yakut literary language: Phonetics and tonio Pérez-Ortiz, Felipe Sánchez-Martínez, Gema morphology). Moscow, Nauka. Ramírez-Sánchez, and Francis M Tyers. 2011. Aper- tium: a free, open-source platform for rule-based ma- Rimma Vasilieva, M Degtyareva, N Ivanova, and L Se- chine translation. J. Machine Translation, 25(2). menova. 2013. Современная этноязыковая си- туация в Республике Саха (Якутия): социопси- James Forsyth. 1994. A history of the peoples of Siberia: холингвистический аспект (Modern ethno-linguistic Russia’s North Asian colony 1581-1990. Cambridge situation in the Republic of Sakha (Yakutia): sociopsy- University Press. cholinguistic aspect). Novosibirsk, Nauka. Roman Yangarber. 2018. Support for endangered and Sardana Ivanova, Francis Tyers, and Jonathan Washing- low-resource languages via e-learning, translation and ton. (To appear). The Apertium implementation of crowd-sourcing. In FEL XXII: Proceedings of the finite-state morphological analysis for the Sakha lan- 22nd Annual Conference of the Foundation for En- guage. dangered Languages, pages 90–97. London: FEL & EL Publishing. Anisia Katinskaia, Sardana Ivanova, and Roman Yangar- ber. 2019. Multiple admissibility: Judging grammat- Viktoria Yartseva. 1990. Лингвистический энцикло- icality using unlabeled data in language learning. In педический словарь (Linguistic encyclopedic dictio- Proceedings of the 7th Workshop on Balto-Slavic Nat- nary). Moscow, Sovetskaya entsiklopediya. ural Language Processing, pages 12–22.
Anisia Katinskaia, Javad Nouri, and Roman Yangarber. 2018. Revita: a language-learning platform at the in- tersection of ITS and CALL. In Proceedings of LREC: 11th International Conference on Language Resources and Evaluation, Miyazaki, Japan.
Anisia Katinskaia and Roman Yangarber. 2018. Digi- tal cultural heritage and revitalization of endangered Finno-Ugric languages. In Proceedings of the 3rd Conference on Digital Humanities in the Nordic Coun- tries, Helsinki, Finland.
Leonid Kharitonov. 1987. Самоучитель якутского языка (Yakut language tutorial). Yakutsk Publishing.
Evdokia Korkina. 1970. Наклонения глагола в якут- ском языке. Moscow, Nauka.
Kimmo Koskenniemi. 1983. Two-level morphology: A general computational model for word-form recogni- tion and production. Ph.D. thesis, Helsinki, Finland.
Nyurgun Leontiev. 2015. The newspaper corpus of the Yakut language. TurkLang-2015, page 233.
Krister Lindén, Erik Axelson, Sam Hardwick, Tommi A Pirinen, and Miikka Silfverberg. 2011. HFST— framework for compiling and applying morphologies. In International Workshop on Systems and Frame- works for Computational Morphology, pages 67–85. Springer.