Transliteration for Low-Resource Code-Switching Texts:
Building an Automatic Cyrillic-to-Latin Converter for Tatar
Chihiro Taguchi∗, Yusuke Sakai∗, and Taro Watanabe{taguchi.chihiro.td0, sakai.yusuke.sr9, taro}@is.naist.jp
Nara Institute of Science and Technology

Abstract
We introduce a Cyrillic-to-Latin transliterator
for the Tatar language based on subword-level
language identification. The transliteration is achallenging task due to the following two reasons. First, because modern Tatar texts often
contain intra-word code-switching to Russian,a different transliteration set of rules needs tobe applied to each morpheme depending on thelanguage, which necessitates morpheme-level
language identification. Second, the fact that
Tatar is a low-resource language, with most of
the texts in Cyrillic, makes it difficult to prepare a sufficient dataset. Given this situation,
we proposed a transliteration method basedon subword-level language identification. We
trained a language classifier with monolingual
Tatar and Russian texts, and applied different
transliteration rules in accord with the identified language. The results demonstrate that our
proposed method outscores other Tatar transliteration tools, and imply that it correctly transcribes Russian loanwords to some extent.

1

Introduction

Modern Tatar has two orthographies: Cyrillic andLatin. The two alphabets are mostly mutually compatible when an input string consists of only Tatarorigin words. Effectively, however, modern Tatar
has a massive amount of Russian loanwords, and,in colloquial texts, even a whole phrase may beswitched to Russian. This linguistic phenomenon
is known as code-switching or code-mixing.
A difficulty of transliteration from Cyrillic toLatin lies in the following two facts. First, a different set of transliteration rules has to be applied
to Tatar and Russian words. This requires a language detection for each token, or worse, for eachmorpheme, which would additionally require morphological analysis. It is expected that a full implementation of such a system will produce heavy processes. Second, because modern Tatar frequently∗

Equal contribution

mixes Russian words, it is not easy to obtain a pureTatar dataset for developing a language detector.
Existing methods are based on either Tatar monolingual rules or a huge bundle of ad-hoc rules aimed
to cover Russian-origin words (Bradley, 2014; Korbanov, n.d.). The experimental results in Section
6 demonstrate that the former monolingual rulebased transliterators show low accuracy because
Russian words are not supported. The latter extensively rule-based transliterator has better accuracy,
but still misses a certain amount of words. Thisimplies that a strictly rule-based method requires
an ever-lasting process of adding rules ad hoc for
exceptional words to further improve the accuracy.
This is obviously unrealistic and inefficient.
In this study, in contrast, we pursue a simple
yet high-accuracy automatic transliteration systemfrom Cyrillic Tatar to Latin Tatar. We prepare two
sets of simple rule-based monolingual transliteration rules for Tatar and Russian each. In addition,
we train a classifier that automatically determines
Tatar or Russian for each subword. Each token
is then transliterated to Latin Tatar by applying
the rules of the detected language to its subwords.
The results demonstrate that our proposed method
achieves higher accuracy than the previous tools.
Also, our proposed method demonstrates higher accuracy than the transliterator with only Tatar-based
rules, indicating that the method can correctly predict and transcribe Russian words to some extent.

2

Tatar: Linguistic Background

Tatar (ISO 639-1 Code: tt) is a Kipchak languageof the Turkic language family mainly spoken in
the Republic of Tatarstan, Russia. The number ofthe speakers is estimated to be around five million(Eberhard et al., 2021). The canonical word order
is SOV, but allows for free word order with scrambling. Morphological inflections and declensions
are derived by means of suffixation. The suffixation obeys the vowel harmony rule where backness

(or frontness) of vowels is kept consistent.
Through the long history of language contact
with Russian, modern Tatar contains a large amountof Russian loanwords. Most of the speakers arebilingual along with Russian, and younger generations living in urbanized cities tend to have better
competence in Russian than in Tatar. This bilingualism leads to frequent code-switching (CS) in
text and speech, particularly of colloquial genre(Izmailova et al., 2018).
2.1

Tatar Orthographies

The mainstream orthography for modern Tatar is
the Cyrillic alphabet, which comprises the Russianstandard alphabet and six extended Cyrillic letters.
Cyrillic Tatar is mostly used by Tatars living in
Russian-speaking countries, while the Latin alphabet is used by Tatars living in other areas such as
Turkey and Finland.
Until 1928, Tatar was exclusively written in the
Arabic script. Along with the latinization projectprevailing in the Soviet Union in 1920–30s, a Latinalphabet system yañalif was introduced to Tatar. In
1939, for political reasons, yañalif was supersededby the Cyrillic alphabet which has been officiallyused in Tatarstan as of now. After the demise of
the Soviet Union, with the resurgence of the movement for restoring the Latin orthography, a new
Latin-based orthography system was adopted bya republic law in 1999 (National Council of the
Republic of Tatarstan, 1999). However, the law
soon lost its validity in 2002 when a new paragraph stipulating that all the ethnic languages in
Russia must be written in Cyrillic was added to
the federal law (Yeltsin, 2020). The current Latinalphabet (2013Latin henceforth) based on the Common Turkic Alphabet was officially adopted by arepublic law in 2013, and is commonly used in
Tatar diaspora communities (National Council of
the Republic of Tatarstan, 2013).
We define that the term “Latin alphabet” usedin this paper refers to 2013Latin. A detailed
rules for the conversion to 2013Latin is given in
Timerkhanov and Safiullina (2019).
2.2

Code-switching in Tatar

An example of the former is displayed in (1) withtransliteration in the Latin alphabet and the translation. Underlined klass (klass “class, grade”) isa Russian word naturalized in Tatar, though it ispronounced with Russian phonology and therefore
requires a different transliteration. In addition, alocative suffix -ta (-ta “at, in”) is attached in theexample, as Russian loanwords may take Tatar suffixes, causing CS within a token (intra-word CS).
(1) Bezneң klassta kyzlar sigezenqedәnqә baxlagan ide.
translit.: Bezneñ klassta qızlar sigezençedän
eçä başlağan ide.
“In our class, girls used to start drinking by theeighth grade.”

3

Related Work

Code-Switching. Even though CS has attracted
researchers in NLP, the lack of resource has beena major difficulty, because CS is an exclusively
colloquial linguistic phenomenon and CS texts are
seldom recorded. Jose et al. (2020) enumerates
a list of available CS datasets at the time of the
publication. In terms of both the availability ofdatasets and the popularity of research, CS language pairs in trend are Hindi–English (Srivastava
et al., 2020, Singh and Lefever, 2020), Spanish–
English (Alvarez-Mellado, 2020, Claeser et al.,2018), Arabic varieties and Modern Standard Arabic (Hamed et al., 2019).
As for the studies of intra-word CS in other languages, Mager et al. (2019) for German–Turkish
and Spanish–Wixarika, Nguyen and Cornips (2016)
for Dutch–Limburgish, and Yirmibeşoğlu and Eryiğit (2018) for Turkish–English have a similar approach to ours. The differences from ours are that
Mager et al. (2019) employs segRNN (Lu et al.,2016) for segmentation and language identification,and that Nguyen and Cornips (2016) uses Morfessor (Creutz and Lagus, 2006) for morphologicalsegmentation. However, our task that combines
language detection of intra-word CS and transliteration has never been undertaken in any of thesestudies.
Tatar Transliteration. At the time of this writing,the following tools are available for Tatar CyrillicLatin conversion. The Tatar Transcription Tool
(TTT henceforth) (Bradley, 2014) is a transliterator published online by Universität Wien as a partof the Mari Web Project. speak.tatar1 is an anonymously developed transliteration service. FinTat2 is
a transliteration tool developed as a part of the Corpus of Written Tatar (Saykhunov et al., 2019). Aylandirow is a strictly rule-based transliteration tool
1
2

https://speak.tatar/en/language/converter/tat/cyrillic/latin
http://www.corpus.tatar/fintat

available online that extensively covers Russianorigin words as well as Tatar-origin (Korbanov,n.d.). The transliteration system employed in FinTat is based on the Latin alphabet used by Tatars inFinland, whose orthography is somewhat different
from 2013Latin.

4

MethodWe transliterate Cyrillic Tatar to Latin Tatar wordby word, as each word does not affect other words
in Tatar transliteration3 .
Taking into account the fact that Tatar has intraword CS, we created a classifier that detects a language (Tatar or Russian) for each subword. To
implement the language classifier, we prepared twomonolingual corpora of Tatar and Russian. Given
the lack of pure Tatar texts without CS in modern
texts4 , we employed Tatar translation of Qur’an5(19,691 words with duplication) translated in 1912
that contains no Russian loanwords in order toavoid noise to train the classifier. Its Russian counterpart6 (21,256 words with duplication) was translated by the Ministry of Awqaf, Egypt.
The training process is as follows. First, thewords collected from the dataset were automatically divided into subwords by the Byte Pair Encoding algorithm (Sennrich et al., 2016). Banerjee and Bhattacharyya (2018) reports that, unlike
Morfessor, BPE can flexibly solve the OOV problem because some subwords are character-levelsegments. In our case, due to the meagerness ofthe monolingual training data, we employed BPE
to avoid the OOV problem. Then, assuming that
longer subwords are less ambiguous with respect tolabels to be assigned, we took the longest match to
make it easier to distinguish between Russian andTatar; for this reason, the subword merge operation was repeated until no further merge was possible. The obtained subwords are then represented insubword embeddings using fastText7 (Bojanowski
et al., 2017). The classification model is the supervised classifier provided by fastText, with thefollowing hyperparameters that are known to per3
The code is available here: https://github.com/naistnlp/tatar_transliteration. A demonstration page is publishedon the website: https://yusuke1997.com/tatar.
4
In the evaluation dataset we prepared for this study, 1,009
words out of 8,466 contained at least one Russian morpheme.
5
https://cdn.jsdelivr.net/gh/fawazahmed0/quranapi@1/editions/tat-yakubibnnugman.json
6https://cdn.jsdelivr.net/gh/fawazahmed0/quranapi@1/editions/rus-ministryofawqaf.json
7https://github.com/facebookresearch/fastText

form high accuracy8 : the number of dimensions is
16, the minimum and maximum character n-gramsizes are 2 and 4. Hierarchical softmax is used as
the loss function. Together with this representation, the subword embeddings are annotated with alanguage label (Joulin et al., 2017).
It is worth noting that, unlike Mager et al. (2019),
we do not employ deep learning approach. While
the task in Mager et al. (2019) is multi-labeling,
our language identification task is a binary classification with a low-resource training dataset; for thisreason, deep learning is too superfluous and heavy
for achieving our task.
To evaluate the performance of our model, weapply BPE also to the test data in the same manner,and predict a language label for each subword. Adjacent subwords are, when possible, combined toform a longer subword with a single language label
for the sake of better accuracy; that is, for example,
when two consecutive subwords are both labeled
as tt, then they are combined into one subword labeled as tt. Finally, each subword is converted to
the Latin alphabet with the transliteration rules ofthe predicted language, and combined into a word
as an output.

5

Experimental Setup

For the evaluation of the performance, we prepared
700 sentences (shuffled; 8,466 words with duplication, 5,261 without duplication) from the Corpus
of Written Tatar (Saykhunov et al., 2019) and theirLatin counterpart as the gold data that was manually transcribed by us and verified by a native
speaker. Also, we annotated the Cyrillic text dataso that CS Russian morphemes are tagged. According to this, the text data contains 1,009 words (withduplication) with a Russian morpheme, and 598
words (with duplication) with intra-word CS.
For evaluation metrics, we calculated BLEU andlongest common sequence (LCS) F-measure foreach letter in a word as well as word accuracy(ACC) and character error rate (CER). The calculation of LCS F-measure and ACC is based on Chenet al. (2018).
To compare the performances of Tatar monolingual transliteration and of Tatar–Russian bilingualtransliteration (i.e., our proposed method; “tt-ru
hybrid” henceforth), we also evaluated the datawith solely Tatar monolingual transliteration rules
8
The training setting was inspired by the blog post by
fastText: https://fasttext.cc/blog/2017/10/02/blog-post.html

BLEU

LCS F-score

CER

ACC

# correct sentence# error word

speak.tatar
TTT
Aylandirow

0.869
0.879
0.971

0.953
0.956
0.994

0.049
0.054
0.009

0.952
0.946
0.991

67
121
362

1,747
1,505
526

tt-based
tt-ru hybrid

0.968
0.981

0.989
0.994

0.011
0.007

0.989
0.993

365
437

552
332

Table 1: The experimental results with 700 sentences (5,261 words without duplication). CER and ACC arecomplementary in probability (i.e., ACC = 1 − CER). Note that the transliteration result is uniquely determinedas the proposed method is rule-based.

(“tt-based” henceforth). For the comparison withthe existing transliteration tools, we computed thescores of speak.tatar, TTT, and Aylandirow9 .

6

Results

As shown in Table 1, the experimental results
demonstrate that our tt-ru hybrid marked the bestscore in all the metrics. CER is the lowest, meaning that the total number of mistakes at a character level is the fewest. The difference is evident
between monolingual transliterators (in particular,
speak.tatar and TTT) that do not support Russianloanwords and bilingual transliterators (Aylandirowand tt-ru hybrid). Between the two groups, there
is more or less a difference by 0.1 points in their
BLEU scores. Furthermore, our monolingual ttbased marked higher scores than the other twomonolingual transliterators. A possible explanation to this gap will be given in the next section.
Compared to Aylandirow, an extensive rulebased transliterator, CER (i.e., ACC also) in ttru hybrid was slightly better by 0.002 points. In
LCS F-Score, in contrast, Aylandirow has the same
score as our tt-ru hybrid. This fact means that Aylandirow returned transliterations somewhat closer
to the gold data than our tt-ru hybrid.
Note that the perfection is not necessarily theultimate goal of this transliteration. As describedin detail in Section 7, there may be several ways ofspelling in actual language use, particularly of thespelling of proper nouns. This variance in spelling
foreign words is common among languages.

7

Analysis

The results illustrated that the bilingual transliterators generally have higher accuracy than the
9

Although we mentioned FinTat in Section 2, we excluded
it from the evaluation because it is based on a spelling systemslightly different from 2013Latin.

monolingual transliterators. However, it needs tobe examined that tt-based also gained higher scoresthan the other monolingual ones, even though it
does not support Russian loanwords. This is dueto the fact that their transliteration rule sometimes
does not follow the rules of 2013Latin. For example, tәnkyt~ “criticism” is transliterated as tänqit’ (with a hamza at the end) by the TTT, whereit should be transliterated as tänqit according to
2013Latin. In fact, due to the de-facto absenceof any institution that regulates the Latin orthography, different varieties in spelling styles can beobserved on the Internet. For this reason, the seemingly high scores in tt-based are merely a productof the orthographical consistency.
Keeping this in mind, the improvement in thescores between tt-based and tt-ru hybrid indicatesthat the bilingual transliteration method designed
to be adaptive to Russian loanwords successfully
predicted the language and transcribed them.
Table 3 illustrates the number of error words categorized with respect to the transliteration rules (ttbased and tt-ru hybrid). We can see from the table
that, among the error words observed in tt-based(V (T )), 336 words were correctly transliterated by
tt-ru hybrid.
Examples of successful transliteration in our ttru hybrid are zakon and sovet shown in the upper
example of Table 2. The tt-based transliteration
converted them as zaqon and sowet, while tt-ru
hybrid correctly returned zakon and sovet. Thisshows that tt-ru hybrid successfully identified thelanguage, since they are Russian loanwords.
However, tt-ru hybrid wrongly transliterated 116
words that were correct in tt-based; for example,
in the lower example of Table 2, the underlined
transliteration soklanıp in tt-ru hybrid is a transliteration error, where the correct word form is soqlanıp. Because the language classifier identified

Sourcett-based
tt-ru hybrid

RF Zakon qygaruqylar sovety prezidiumy utyryxynda katnaxty.
RF Zaqon çığaruçılar sowetı prezidiumı utırışında qatnaştı.
RF Zakon çığaruçılar sovetı prezidiumı utırışında qatnaştı.

Sourcett-based
tt-ru hybrid

Һәr lqy tuktap, anyң hozurlygyna soklanyp kitә.
Här yulçı tuqtap, anıñ xozurlığına soqlanıp kitä.
Här yulçı tuqtap, anıñ xozurlığına soklanıp kitä.

Table 2: Examples of successful and unsuccessful transliterations in tt-ru hybrid based on subword tokenization.
The underlined words are error words (Russian loanwords). The upper sentence is an example where tt-ru hybrid
can correctly transliterate the underlined words while tt-based cannot. The lower example is, on the other hand,
tt-ru hybrid mistakenly transliterates the underlined word that is correctly transliterated by tt-based.
set (total 5,261 words)
V (T )V (H)
V (T ) ∩ V (H)V (T ) \ V (H)V (H) \ V (T )# words
552
332
216
336
116

Table 3: Comparison of the number of error words(without duplication) between the monolingual ttbased transliteration and our proposed tt-ru hybridtransliteration. V (T ) is the set of error words observed
in tt-based, V (H) in tt-ru hybrid, V (T )∩V (H) in bothtt-based and tt-ru hybrid, V (T ) \ V (H) is the set of error words observed only in tt-based, and V (H) \ V (T )only in tt-ru hybrid.

the first subword as Russian, the Russian transliteration rule was applied, whereas in fact it is not a
Russian loanword.
As for the high LCS F-score in Aylandirow(0.994), it implies that Aylandirow is good at correctly transcribing frequent words including Russian loanwords. Because Aylandirow is strictly
rule-based without automatic language detection,
it can easily suffer from the rule coverage problem;for example, the word taksi (taksi, a Russian loanword) was mistakenly transliterated as taqsi.
As the comparison of performance with respect
to Russian CS words in Table 4 shows, tt-ru hybrid demonstrated higher accuracy in transliterating words with Russian morphemes10 . In particular,
the tt-ru hybrid’s performance adaptive to Russianmorphemes is clearly visible in the accuracies oftranscribing words containing Russian, where tt-ru
hybrid scored 78.1% and tt-based 46.7%.
Considering that Russian CS in Tatar may arbitrarily occur, our proposed method with automatic
10
In this respect, speak.tatar scores the best for Russianwords. This is merely because its transliteration rules aredesigned to work well for Russian words, and, in contrast, its
performance to Tatar words is poor as shown in Table 1.

ru words

CS words#

accuracy#

accuracy

speak.tatar
TTT
Aylandirow

805
258
738

0.798
0.256
0.731

455
175
461

0.752
0.283
0.762

tt-based
tt-ru hybrid

471
788

0.467
0.781

294
464

0.486
0.767

Table 4: A comparison of performance in Russian CS
words. The left column (ru words) demonstrates thenumber of correctly transcribed words that contain Russian and its accuracy that is given by dividing by the
total Russian words (1,009 with duplication). The rightcolumn (CS words) contains the number of correct transcriptions out of 605 words (with duplication) and its
accuracy with respect to intra-word CS words.

language detection is expected to show a stable performance to any Russian words; in contrast, rulebased systems such as Aylandirow are less flexible
to unknown Russian words, which, in effect, exist
infinitely in natural languages as hapax legomena.

8

Conclusion

In this paper, we proposed a new transliteration
system that converts Cyrillic Tatar to Latin Tatar.
Taking into account the facts that different transliteration rules are applied to Russian loanwords andthat intra-word CS is frequently observed, our proposed method involved language identification foreach subword. Even though Tatar resources available at hand for training were limited, the results
were significantly better than existing transliteration tools. The simple architecture of language
detection employed in this approach is languageagnostic does not need detailed analyses such assyntactic parsing and POS tagging, our method
is applicable to other low-resource languages thathave intra-word CS.

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A

Appendix
Cyrillic
a
b
v
g
d
e


z
i

k
l
m
n
o
p
s
t

Latin (Tatar)
a
b
w
g, ğ
d
e, ye, yı
NA
j
z
i
y
k, q
l
m
n
o
p
s
t

Latin (Russian)
a
b
v
g
d
e
yo
j
z
i
y
k
l
m
n
o
p
s
t

Cyrillic
u
f
h
c
q
x
w

y
~


ә
ө
ү
җ
ң
һ

Latin (Tatar)u, uw, w
f
x
NA
ç
ş
NA
—
ı
—
e, ’
yu, yü, yuw, yüw
ya, yääöü, üw, wcñhLatin (Russian)
u
f
x
ts
ç
ş
şç
—
ı
—
e
yu
ya
NA
NA
NA
NA
NA
NA

Table 5: Tatar’s Cyrillic–Latin correspondence for Tatar- and Russian-origin words. NA (not applicable) meansthat the letter does not appear in the language. An em-dash means that the letter is ignored in Latin transcription.

original
gold (Latin)

RF Prezidenty Vladimir Putin Rossi mөselmannaryn izge Ramazan
RF Prezidentı Vladimir Putin Rossiyä möselmannarın izge Ramazan

speak.tatar
TTT
Aylandirow

RF Prezidentı Vladimir Putin Rossiyä möselmannarın izge Ramazan
RF Prezidentı Wlädimir Pütin Rössiyä möselmannarın izge Ramazan
RF Prezidentı Vladimir Putin Rossiä möselmännarın izge Ramazan

tt-based
tt-ru hybrid

RF Prezidentı Wladimir Putin Rossiyä möselmannarın izge Ramazan
RF Prezidentı Vladimir Putin Rossiyä möselmannarın izge Ramazan

Table 6: An example of comparison of transliterations. The sequence on the top is the original corpus sentencein Cyrillic, below which is the Latin counterpart manually transcribed. The three sentences in the middle row aretransliterations by the previous tools. The first sentence in the bottom row is the monolingual tt-based transliteration, and the second is transcribed by our proposed method.