UD Tatar
C. Taguchi
Abstract
Introduction
Tatar

Universal Dependencies Treebank for Tatar
Incorporating Intra-Word Code-Switching Information

Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work

Chihiro Taguchi12

Sei Iwata1

Taro Watanabe1

CorpusExperiment
Results

1
Nara Insitute of Science and Technology{taguchi.chihiro.td0, iwata.sei.is6, taro}@is.naist.jp

Conclusion
2
Acknowledgments

University of Edinburgh

References
References

Workshop on Resources and Technologies for Indigenous,
Endangered and Lesser-resourced Languages in Eurasia (EURALI)
June 20, 2022

1 / 31

Abstract
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work

Aim
Build language resources for code-switching minority languages
with the case study in Tatar

CorpusExperiment
Results
Conclusion
Acknowledgments
References

Universal Dependencies Tatar NMCTT Treebank
Introducing annotation for intra-word code-switching
Evaluating the usefulness of the intra-word code-switchingannotation in UD

References

2 / 31

Table of Contents
UD TatarC. Taguchi
Abstract1

Abstract

2

Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal Dependencies

3

Related Work

4

Corpus5

Experiment

6

Results

7

Conclusion

8

Acknowledgments

9References

Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments
References
References

3 / 31

Motivation
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment

Why Tatar?
NLP has centered around only a handful of “big” languages
out of approx. 6,000 languages in the world
Minority languages have been marginalized because they
are low-resource
Minority languages often exhibit code-switching (CS)Results
Conclusion
Acknowledgments
References

Tatar: low-resource language with intra-word CS
A case study on such a language enriches and encourages
corpus building for marginalized languages

References

4 / 31

Backgrounds: Tatar
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords

Spoken in Tatarstan, Russia by 5–7 mil. people
Tatar < Kipchak (Northwestern) < Turkic
SOV, head-final
asymmetric bilingualism1 : CS with Russian, language shift
Cyrillic ortography (+ Latin)

Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments
References
References

Figure: Distribution of the Turkic languages. Light green in the northwest indicates the
Tatar-speaking area. Map modified from the original one by Wikimedia Commons.
1 Safina (2020) “Bilingualism in the Republic of Tatarstan: language policy and attitudes towards Tatar language education”
5 / 31

Backgrounds: Code-Switching (CS)UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching

Code-Switching (CS)2
A sociolinguistic phenomenon where a speaker switches alanguage to another, typically from a local, minority language
variety to a prestigious, majority one

CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments

Intra-word CS: CS inside a word, often found inmorphologically-rich languages
Tagalog – English (Philippines)
magtextan (‘to text each other’)Huichol – Spanish (Mexico)
pecansadoxi (‘you are tired’) 3

References
References

Tatar – Russian (Russia)прививканы (‘vaccination’- ACC4 )privivka-nı (transliteration)
2 Alvanoudi (2017) “Language contact, borrowing and code switching: a case study of Australian Greek”
3 Mager, Çetinoğlu, and Kann (2019) “Subword-Level Language Identification for Intra-Word Code-Switching”
4 ACC: accusative.
6 / 31

Code-Switching vs. Loanwords
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results

How is code-switching different from loanwords (in Tatar)?
Switch from the Tatar phonology to the Russian phonology“Contact-induced speech behavior that occurs extensively inthe talk of bilinguals”5
Language-specific merit: transliteration from Cyrillic to Latin6

Conclusion
Acknowledgments
References
References

5 Alvanoudi (2017) “Language contact, borrowing and code switching: a case study of Australian Greek”
6 Taguchi, Sakai, and Watanabe (2021) “Transliteration for Low-Resource Code-Switching Texts: Building an Automatic
Cyrillic-to-Latin Converter for Tatar”
7 / 31

Backgrounds: Universal Dependencies (UD)UD TatarC. Taguchi
Abstract
Introduction

Universal Dependencies (UD)7
A project for building multilingual annotated treebanks in theunified CoNLL-U format

Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Covering more than 200 treebanks of 130 languages
No treebanks for Tatar before this study

Related Work
obl
root

CorpusExperiment
Results
nmod

punct

Conclusion
amod

Acknowledgments

obj

aux

References
ReferencesТатарстанда
PROPN
Tatarstan-LOCкоронавирустан
NOUN
coronavirus-ABL

беренче
ADJ
firstпрививканы
NOUN
vaccination-ACCясатыргаVERB
make-CAU - INF

мөмкин
AUX
possible.
PUNCT

Figure: Example of a visualized CoNLL-U tree
7 Nivre et al. (2020) “Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection”
8 / 31

Backgrounds: Universal Dependencies (UD)UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusRich grammatical information
FORM: Word form (token)
LEMMA: Lemma
UPOS: Part-of-speech
FEAT: Morphological information
HEAD: Syntactic head
DEPREL: Dependency relation
MISC: Miscellaneous information (+ language tag)Experiment
Results
Conclusion
Acknowledgments
References
References

Figure: Example of UD annotation in Tatar.
9 / 31

Related Work: CS and Language Resources
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-SwitchingFirst language resources for CS:
Lyu et al. 2010: Mandarin–English
Li, Yu, and Fung 2012: Mandarin–English

CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments
References
References

CS language resources in UD:
UD Turkish-German SAGT (Çetinoğlu 2016) (intra-word CS)
UD Hindi-English HIENCS (Bhat et al. 2018) (word-level CS)

Language resources of Tatar:
The Corpus of Written Tatar (Saykhunov et al. 2021): 356
million tokens
The Tatar National Corpus (Suleimanov et al. 2013): 180
million tokens
No manually annotated treebank of Tatar

10 / 31

Related Work: Available Turkic Treebanks
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments

Turkish-German SAGT (Çetinoğlu 2016): 37K tokens
Kazakh KTB (Makazhanov et al. 2015): 10K tokens
Old Turkish Tonqq (Derin and Harada 2021): 221 tokens
Uyghur UDT (Eli et al. 2016): 40k tokens
Yakut YKTDT (Merzhevich and Gerardi 2021): 271 tokens+ 9 Turkish treebanks (733K tokens in total)
Turkic languages in UD are overall low-resourced except Turkish.

References
References

11 / 31

Related Work: Language Processing for CS
UD TatarC. Taguchi
Abstract
Introduction

Earliest work: Joshi 1982 (Marathi–English)

Tatar
Code-Switching

CS point prediction: Solorio and Liu 2008

CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments

POS tagging: Anastasopoulos et al. 2018 (Griko–Italian)
Intra-word CS:
Mager, Çetinoğlu, and Kann 2019 (Wixarika–Spanish) with
SegRNN8
Sabty et al. 2021 (Arabic–English)
Taguchi, Sakai, and Watanabe 2021 (Tatar–Russian) with
BPE9

References
References

8 Lu et al. (2016) “Segmental Recurrent Neural Networks for End-to-end Speech Recognition”
9 Sennrich, Haddow, and Birch (2016) “Neural Machine Translation of Rare Words with Subword Units”
12 / 31Overview of NMCTTUD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords

UD Tatar-NMCTT: The first UD treebank of Tatar
First release: November 2021

Universal
Dependencies

Latest version: UD v2.10 (May 2022)

Related Work

Original text: Online news media (Tatar-Inform)

CorpusExperiment

Annotator: Chihiro Taguchi

Results

Size: 1,458 tokens

Conclusion
Acknowledgments
References

Repository: https://github.com/
UniversalDependencies/UD_Tatar-NMCTT

References

13 / 31

NMCTT: tokenization
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching

Tokenization by splitting at spaces and punctuation
Locative adjectivizer: different token?
SAGT: different token (cf. 1)
NMCTT: same token (cf. 2)

CS vs. Loanwords
Universal
Dependencies

Related Work
Corpus

ID
1-2
1
2

FORM
Berlin’deki
Berlin’de
ki

LEMMA
_
Berlin
ki

UPOS
_
PROPN
ADP

Experiment
Results

Table: Tokenization and tags in SAGT.

Conclusion
Acknowledgments
References
References

ID
1

FORM
Берлиндагы

LEMMA
БерлинUPOS
PROPN

Table: Tokenization and tags in NMCTT (transliterated).

14 / 31

NMCTT: Part-of-Speech
UD TatarC. Taguchi
Abstract

Disproportional distribution of CS w.r.t. POS tags
Class

Introduction
Tatar
Code-SwitchingOpen

CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment

ClosedResults
Conclusion
Acknowledgments
References
References

Other

UPOS
NOUN
PROPN
VERB
ADJ
AUX
DET
ADV
SCONJ
ADP
CCONJ
PRON
NUM
PUNCT

Total
413
79
169
117
18
9
40
8
35
26
26
12
167

Russian
21
34
0
8
0
0
0
0
0
0
0
0
0

Mixed
62
8
1
0
0
0
0
0
0
0
0
0
0

Table: The distribution of UPOS tags in the treebank with respect tolanguage code. The first column specifies whether the UPOS tag is an
open class or a closed class.

15 / 31

NMCTT: Morphology
UD TatarC. Taguchi
Abstract
Introduction

PolicyDifferent morphemes with different semantics have different
morphological features.

Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments
References
References

Morphological annotation of Turkic treebanks has been
inconsistent
Example: Converbs (Haspelmath 1995)
Annotation in NMCTT in Table
See Table 5 of the paper for the variation of converb annotation
among Turkic treebanks
Converb Form-п-а/ә-гач/гәч
-ганчы/гәнче

Semantics
Associated event
Simultaneous event
Completed event
Unaccomplished event

FeaturesVerbForm=Conv
Aspect=Prog|VerbForm=Conv
Aspect=Perf|VerbForm=Conv
Aspect=Imp|VerbForm=Conv

Table: Tatar converbs and their morphological features in NMCTT
16 / 31

NMCTT: Syntactic Dependency
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

NMCTT follows other UD treebanks’ policies for syntactic
dependency.
Language-specific treatments:
Light Verb Construction: compound:lvc
Verbs grammaticalized into auxiliaries: aux

Related Work
root

root

CorpusExperiment
nsubj

nsubj

Results
Conclusion

obj

compound:lvc

aux

Acknowledgments
References

дәрес

дәвам

итте

ул

китапны

укып

чыкты

References

Figure: An example ofcompound:lvc.

Figure: An example of agrammaticalized auxiliary aux.

17 / 31

NMCTT: Language Tags LangID=
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperimentTatar CS with Russian can be within a word (intra-word CS):Гыйбәтдиновка (Russian in red)Ğıybätdin-ov-qa‘To Gibatdinov’
Annotation of intra-word CS proposed in the treebank:
CSPoint=Гыйбәтдин§ов§ка|LangID=MIXED[TT§RU§TT]

Results
Conclusion
Acknowledgments
References

CSPoint= indicates the point(s) of language switch
LangID= is tagged as MIXED, and the details are specified
in brackets with ISO language codes

References

18 / 31Experiment
UD TatarC. Taguchi
Abstract

Issue

Introduction
Tatar
Code-Switching
CS vs. Loanwords

Does the intra-word CS annotation in UD contribute to correct
prediction of CS segmentation and language identification?

Universal
Dependencies

Related Work
CorpusExperiment
Results

Task Description
Predict a pair of language tag and span tag at the character levelutilizing the corresponding POS tag

Conclusion
Acknowledgments
References
References

Dataset:UD Tatar-NMCTT (1,119 tokens)
UD Turkish-German-SAGT (26,929 tokens) (Çetinoğlu 2016).

19 / 31ModelUD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusConditional Random Fields (CRFs)10 : loose description
Probabilistic graphical model
Able to consider context
Suitable for labeling sequential data where each data point
depends on its previous data

Experiment
Results
Conclusion
Acknowledgments
References
References

Intuitions:
CS depends on context
CS depends on part-of-speech; open-class words (e.g.,
nouns, verbs etc.) are more likely to code-switch thanclosed-class words (e.g., pronouns, auxiliaries)

10 Lafferty, McCallum, and Pereira (2001) “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling
Sequence Data”
20 / 31ModelUD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusConditional Random Fields (CRFs)
To predict correct labels y (language tag and span tag) given asequence of input x (character-level in this task), the CRF model
is defined as
( K
)
X
1
exp
λk fk (yt , yt−1 , xt )p(y|x) =
Z (x)
k =1

Experiment
Results
Conclusion
Acknowledgments
References
Referenceswhere:nPoPK
Z (x) = y exp
k =1 λk fk (yt , yt−1 , xt ) : a normalization
function to ensure the sum of p is 1;
λk : a parameter vector; and{fk }Kk=1 : a set of feature functions.
21 / 31ModelUD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related WorkK : the total number of features after combining transition
features fij (y , y ′ , x) = 1{y =i} 1{y ′ =j} for each transition (i, j) andobservation features fio (y , y ′ , x) = 1{y =i} 1{x=o} for eachstate-observation pair (i, o).
Namely,CorpusK
X

Experiment
Results

λk fk (yt , yt−1 , xt )Conclusion

k =1

Acknowledgments

XX

References
References

=

t

i,j

λij fij (yt , yt−1 , xt ) +X
λio fio (yt , yt−1 , xt ) .

i,o

22 / 31

Feature table
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments
References

We used features that might affect the model. In particular, inorder to verify the usefulness of UD’s grammatical information in
the prediction task, the POS feature is added in the feature
extraction function.
FeatureCharacter
Character +1
Character +2
Character -1
Character -2
Word-initial?
Word-final?
Word in titlecase?
Character in uppercase?
Punctuation?
Number?
Word length
POS
Word

Example
"M"?
"a"?
"r"?
False?
False?
True?
False?
True
True
False
False
4
"PROPN""Mars"

Value
1
1
1
1
1
1
1
1
1
0
0
1
1
1

References

Table: An example of a feature table for the character "M" in "Mars".
Features in red are omitted in ablation studies.

23 / 31

Results
UD TatarC. Taguchi
Abstract
Introduction
Tatar

Highest scores in the Default models[-POS]: without the POS feature[-word]: without the Word feature[-POS, -word]: without both the POS and Word features

Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusFeatures

Precision

RecallF1

Default[-POS][-word][-POS, -word]

90.9
87.3
86.4
86.7

90.0
86.5
86.5
87.0

88.9
84.3
84.9
85.7

Experiment
Results

Table: Ablation study of features on NMCTT. Scores are calculated at the character level.

Conclusion
Acknowledgments
References

Features

Precision

RecallF1

References

Default[-POS][-word][-POS, -word]

95.9
95.9
94.6
93.7

96.1
95.8
94.7
93.9

95.9
95.6
94.6
93.8

Table: Ablation study of features on SAGT. Scores are calculated at the character level.
24 / 31

Results
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusDefault (w/ POS, word) performs the best both in NMCTT
and SAGT
Excluding POS information worsens the performance in both
NMCTT and SAGT

Experiment
Results
Conclusion

The results imply that POS is meaningful in CS segmentationand language identification

Acknowledgments
References
References

25 / 31

Concluding Remarks
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusThis series of studies has shown:
NMCTT: the first UD treebank for Tatar
the new way to annotate intra-word CS
the usefulness of the CS annotation in applied tasks
For future work:

Experiment
Results
Conclusion
Acknowledgments
References
References

Enlarging the corpus: NMCTT is still too small to be used for
wider applications (at least ~10k tokens)
Encouraging further corpus building for low-resource
languages with CS

26 / 31

Acknowledgments
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
CorpusExperiment
Results
Conclusion
Acknowledgments

This study was a part of Creative and International
Competitiveness Project (CICP) in 2021 and funded by
NAIST.
The right to exploit the news text data used in the NMCTT
treebank is granted by Tatar-Inform.
This research would have never been as it is now without these
supports.

References
References

27 / 31

Bibliographical References I
UD TatarC. Taguchi

Alvanoudi, Angeliki (2017). “Language contact, borrowing and code switching: a case study of Australian Greek”. In:
pp. 1–42.

Abstract
Introduction
Tatar

Anastasopoulos, Antonios et al. (2018). “Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian
Resource”. In: Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe, New Mexico, USA:
Association for Computational Linguistics, pp. 2529–2539. URL: https://aclanthology.org/C18- 1214.

Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work

Bhat, Irshad et al. (2018). “Universal Dependency Parsing for Hindi-English Code-Switching”. In: Proceedings of the 2018
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,
Volume 1 (Long Papers). New Orleans, Louisiana: Association for Computational Linguistics, pp. 987–998. DOI:
10.18653/v1/N18- 1090. URL: https://aclanthology.org/N18- 1090.

CorpusÇetinoğlu, Özlem (2016). “A Turkish-German Code-Switching Corpus”. In: Proceedings of the Tenth International

Experiment

Conference on Language Resources and Evaluation (LREC’16). Portorož, Slovenia: European Language Resources
Association (ELRA), pp. 4215–4220. URL: https://aclanthology.org/L16- 1667.

Results
Conclusion
Acknowledgments

Derin, Mehmet Oguz and Takahiro Harada (2021). “Universal Dependencies for Old Turkish”. In: Proceedings of the Fifth
Workshop on Universal Dependencies (UDW, SyntaxFest 2021). Sofia, Bulgaria: Association for Computational Linguistics,
pp. 129–141. URL: https://aclanthology.org/2021.udw- 1.11.

References

Eli, Marhaba et al. (2016). “Universal dependencies for Uyghur”. In: Proceedings of the Third International Workshop on

References

Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for
Human Language Technologies (WLSI/OIAF4HLT2016). Osaka, Japan: The COLING 2016 Organizing Committee,
pp. 44–50. URL: https://aclanthology.org/W16- 5206.
Haspelmath, Martin (1995). “The converb as a cross-linguistically valid category”. In: Converbs in Cross-linguistic
Perspective: Structure and Meaning of Adverbial Verb Forms — Adverbial Pariticples, Gerunds —. Ed. by Martin Haspelmathand Ekkehard König, pp. 1–55.

28 / 31

Bibliographical References II
UD TatarC. Taguchi

Joshi, Aravind K. (1982). “Processing of Sentences With Intra-Sentential Code-Switching”. In: Coling 1982: Proceedings of
the Ninth International Conference on Computational Linguistics. URL: https://aclanthology.org/C82- 1023.

Abstract

Lafferty, John D., Andrew McCallum, and Fernando C. N. Pereira (2001). “Conditional Random Fields: Probabilistic Models

Introduction

for Segmenting and Labeling Sequence Data”. In: Proceedings of the Eighteenth International Conference on Machine
Learning. ICML ’01. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., pp. 282–289. ISBN: 1558607781.

Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Li, Ying, Yue Yu, and Pascale Fung (2012). “A Mandarin-English Code-Switching Corpus”. In: Proceedings of the EighthInternational Conference on Language Resources and Evaluation (LREC’12). Istanbul, Turkey: European Language
Resources Association (ELRA), pp. 2515–2519. URL:
http://www.lrec- conf.org/proceedings/lrec2012/pdf/964_Paper.pdf.

Related Work
Corpus

Lu, Liang et al. (2016). “Segmental Recurrent Neural Networks for End-to-end Speech Recognition”. In: CoRR
abs/1603.00223. arXiv: 1603.00223. URL: http://arxiv.org/abs/1603.00223.

Experiment
Results

Lyu, Dau-Cheng et al. (2010). “SEAME: a Mandarin-English code-switching speech corpus in South-East Asia”. In:
INTERSPEECH.

Conclusion
Mager, Manuel, Özlem Çetinoğlu, and Katharina Kann (2019). “Subword-Level Language Identification for Intra-Word
Acknowledgments
References
References

Code-Switching”. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for
Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota:
Association for Computational Linguistics, pp. 2005–2011. DOI: 10.18653/v1/N19- 1201. URL:
https://aclanthology.org/N19- 1201.
Makazhanov, Aibek et al. (2015). “Syntactic Annotation of Kazakh: Following the Universal Dependencies Guidelines. A
report”. In: 3rd International Conference on Turkic Languages Processing, (TurkLang 2015), pp. 338–350.
Merzhevich, Tatiana and Fabrício Ferraz Gerardi (2021). UD Yakut YKTDT.
https://github.com/UniversalDependencies/UD_Yakut- YKTDT.

29 / 31

Bibliographical References III
UD TatarC. Taguchi
Nivre, Joakim et al. (2020). “Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection”. English. In:
Abstract
Introduction

Proceedings of the 12th Language Resources and Evaluation Conference. Marseille, France: European Language
Resources Association, pp. 4034–4043. ISBN: 979-10-95546-34-4. URL:
https://aclanthology.org/2020.lrec- 1.497.

Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work

Sabty, Caroline et al. (2021). “Language Identification of Intra-Word Code-Switching for Arabic–English”. In: Array 12,
p. 100104. ISSN: 2590-0056. DOI: https://doi.org/10.1016/j.array.2021.100104. URL:
https://www.sciencedirect.com/science/article/pii/S2590005621000473.
Safina, Kamila (2020). “Bilingualism in the Republic of Tatarstan: language policy and attitudes towards Tatar language
education”. In.

Corpus
Saykhunov, M. R. et al. (2021). Corpus of Written Tatar. URL: https://www.corpus.tatar/en.
Experiment
Results
Conclusion
Acknowledgments

Sennrich, Rico, Barry Haddow, and Alexandra Birch (2016). “Neural Machine Translation of Rare Words with Subword
Units”. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Berlin, Germany: Association for Computational Linguistics, pp. 1715–1725. DOI: 10.18653/v1/P16- 1162. URL:
https://aclanthology.org/P16- 1162.

References

Solorio, Thamar and Yang Liu (2008). “Learning to Predict Code-Switching Points”. In: Proceedings of the 2008 Conference

Referenceson Empirical Methods in Natural Language Processing. Honolulu, Hawaii: Association for Computational Linguistics,
pp. 973–981. URL: https://aclanthology.org/D08- 1102.
Suleimanov, Dz. et al. (2013). “National Corpus of the Tatar Language “Tugan Tel”: grammatical annotation andimplementation”. In: Procedia - Social and Behavioral Sciences 95, pp. 68–74.

30 / 31

Bibliographical References IV
UD TatarC. Taguchi
Abstract
Introduction
Tatar
Code-Switching
CS vs. Loanwords
Universal
Dependencies

Related Work
Corpus

Taguchi, Chihiro, Yusuke Sakai, and Taro Watanabe (2021). “Transliteration for Low-Resource Code-Switching Texts:
Building an Automatic Cyrillic-to-Latin Converter for Tatar”. In: Proceedings of the Fifth Workshop on Computational
Approaches to Linguistic Code-Switching. Online: Association for Computational Linguistics, pp. 133–140. DOI:
10.18653/v1/2021.calcs- 1.18. URL: https://aclanthology.org/2021.calcs- 1.18.

Experiment
Results
Conclusion
Acknowledgments
References
References

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