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

Introduction
Automatic transliteration tool from Cyrillic Tatar to Latin Tatar

Try me! (Demo page)Коронавирус инфекциясеннəн прививкага ясатырга килгəндə үзең белəн паспорт, полис, СНИЛС булырга тиеш.

Koronavirus infektsiyäsennän privivkağa yasatırğa kilgändä üzeñ belän pasport, polis, SNİLS bulırğa tiyeş.
* Words highlighted in blue are Russian. Source:

Two difficulties:
1.
2.

https://tatar-inform.tatar/news/health/31-05-2021/kazanda-yuz
hnyy-s-d-z-gend-vaktsinatsiya-punkty-kabat-achyldy-5825091

Different transliteration rules for Tatar-origin words and Russian words
Low resource for training a language identifier

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Tatar: Linguistic Background
Tatar < Kipchak < Turkic-

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Spoken by 5 million people
Mainly in the Republic of Tatarstan, Russia
Commonly written in Cyrillic, while some diaspora
communities write with the Latin orthography
SOV, head-final, agglutinative

Long history of language contact with Russian-Most speakers are bilingual with RussianFrequent Russian loanwords and code switching

Distribution of Turkic languages. Light green
indicates the Tatar-speaking area.
Source: Wikimedia Commons3

Two Orthographies in Modern Tatar
Mainstream: Cyrillic
Russian alphabet + 6 extended letters (ə, ө, ү, ң, җ, һ)

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Some diaspora communities: Latin
Based on the Common Turkic alphabet (English alphabet + ä, ı, ö, ü, ñ, ğ, ş, ç)
History-Before 1928: Arabic-based orthography
1920–39: Yañalif Latin alphabet
1939–: Current Cyrillic alphabet
1999: New Latin orthography proposed, rejected in 2002
2013–: Current Latin alphabet
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Tatar: Code Switching
Language contact, bilingualism, language shift in the Tatar-speaking society-Try me! (Demo page)

Frequent code-switchinge.g., (blue words are Russian)Башкаурын

табарга

даҗиңелтүгел.

Алданзабронировать

надо.

Başqaurıntabarğa

daciñeltügel.

Aldan

zabronirovat

nado.

otherplace

to find

also

easy

notbeforehand

to booknecessary“It is not easy to find another place. It is necessary to book beforehand.”-Intra-word code-switching (mixed morpheme)e.g., Əхмəтдиновка (Äxmätdinovqa) “To Äxmätdinov”
5Related Work: Code Switching
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CS: Recent trends in NLP-Srivastava+ 2020, Singh&Lefever 2020, etc.:
Alvarez-Mellado 2020, Claeser+ 2018, etc.:
Hamed+ 2019, Samih&Maier 2016, etc.:

Indic languages – English
Spanish – English
Colloquial Arabic – MSA

Intra-word CS-

Mager+ 2019: German–Turkish, Spanish–Wixarika (segRNN: Lu+ 2016)
Nguyen&Cornips 2016: Dutch–Limburgish (Morfessor: Creutz&Lagus 2006)
Yirmibeşoğlu&Eryiğit 2018: Turkish–English (Character n-gram, CRF)

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Related Work: Tatar Transliteration
Transliteration tools:-

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Tatar Transcription Tool (TTT) (Bradley 2014)
Rule-based, Tatar words only
speak.tatar (anonymous)
Rule-based, Tatar words only
FinTat (Saykhunov+ 2019)
Rule-based, using Finnish-Tatars’ orthography
Aylandirow (Korbanov, n.d.)
Rule-based, both Tatar and Russian words

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MethodTrain a binary language classifier: Tatar or Russian---

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Monolingual corpus: Qur’an translations
Tatar: 19,691 words w/ dup.
Russian: 21,256 words w/ dup.
Subword tokenization
BPE (Sennrich 2016)
Robust to the OOV problem
Embedding for classification
FastText (Bojanowski+ 2017,Joulin+ 2017)8

Experimental Setup
Evaluation data-

700 sentences from the Corpus of Written Tatar (Saykhunov+ 2019)--

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shuffled; 8,466 words w/ duplication
not public

Corresponding sentences manually transcribed into the Latin alphabet-

Russian morphemes are tagged

Evaluation metrics-Character BLEU, longest common sequence (LCS) F-measure (Chen+ 2018),
word accuracy (ACC), character error rate (CER)
Baselines: speak.tatar, TTT, Aylandirow
Proposed method: tt-ru hybrid+ our Tatar-monolingual rule-based transliteration (tt-based)

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Results
Try me! (Demo page)tt-ru hybrid outscores the baselines-Aylandirow’s high score in the LCS F-score
Aylandirow’s transliteration is closer to the gold data character-wise
Low scores in monolingual transliterators (speak.tatar, TTT, tt-based)
tt-based’s high score is merely a result of the orthographic consistency
10Analysis (1)Try me! (Demo page)

Difference among monolingual transliterators:-Some rules in speak.tatar and TTT do not follow the current Latin orthography
Orthographic variations are actually observed among users

11Analysis (2)Try me! (Demo page)-Improvement from tt-based to tt-ru hybrid
Accuracy:
Russian words: 46.7% to 78.1%
intra-CS words: 48.6% to 76.7%
tt-ru hybrid can detect some Russian words

A performance comparison for Russian CS words.
High accuracy of speak.tatar arises from the similarity ofits translit. rules to Russian words. Instead, it fails totransliterate Tatar words correctly.

The underlined words are wrong in tt-based but are successfully transliterated in tt-ru hybrid
12Analysis (3): Negative result
Try me! (Demo page)-Some Tatar words were mistakenly identified as Russian
tt-ru hybrid mistakenly transliterated 116 words
that were correct in tt-based-Similar character sequences may cause confusion
in language classification

V(T): set of error words in tt-based
V(H): set of error words in tt-ru hybrid

The underlined word is wrong in tt-ru hybrid but is correctly transliterated in tt-based
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Conclusion
Transliteration quality-

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our tool has the highest accuracy in transliteration overall
it detects Russian morphemes successfully to some extent

Advantages of our method-The model is trained only on low-resource corpus
It employs language-agnostic approaches (BPE, fastText)
Applicable to other CS language pairs

14References
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Elena Alvarez-Mellado. 2020. An annotated corpus of emerging anglicisms in Spanish newspaper headlines.
Tamali Banerjee and Pushpak Bhattacharyya. 2018. Meaningless yet meaningful: Morphology grounded subword-level NMT.
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information.
Jeremy Bradley. 2014. Tatar transcription tool. URL: https://www.univie.ac.at/maridict/site-2014
Nancy Chen, Rafael E. Banchs, Min Zhang, Xiangyu Duan, and Haizhou Li. 2018. Report of NEWS 2018 named entity transliteration shared task.
Daniel Claeser, Samantha Kent, and Dennis Felske. 2018. Multilingual named entity recognition on Spanish–English code-switched tweets using support
vector machines.
Mathias Creutz and Krista Lagus. 2006. Morfessor in the morpho challenge.
Injy Hamed, Moritz Zhu, Mohamed Elmahdy, Slim Abdennadher, and Ngoc Thang Vu. 2019. Code-switching language modeling with bilingual wordembeddings: A case study for Egyptian Arabic–English.
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017. Bag of tricks for efficient text classification.
Dinar Korbanov. n.d. Aylandirow. URL: http://aylandirow.tmf.org.ru
Liang Lu, Lingpeng Kong, Chris Dyer, Noah A. Smith, and Steve Renals. 2016. Segmental recurrent neural networks for end-to-end speech recognition.
Manuel Mager, Özlem Çetinoğlu, and Katharina Kann. 2019. Subword-level language identification for intra-word code-switching.
Dong Nguyen and Leonie Cornips. 2016. Automatic detection of intra-word code-switching.
Mansur R. Saykhunov, R. R. Khusainov, T. I. Ibragimov, J. Luutonen, I. F. Salimzyanov, G. Y. Shaydullina, and A. M. Khusainova. 2019. Corpus ofWritten Tatar.
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units.
Pranaydeep Singh and Els Lefever. 2020. Sentiment analysis for Hinglish code-mixed tweets by means of cross-lingual word embeddings.
Abishek Srivastava, Kalika Bali, and Monojit Choudhury. 2020. Understanding script-mixing: A case study of Hindi–English bilingual Twitter users.
Zeynep Yirmibeşoğlu and Gülşen Eryiğit. 2018. Detecting code-switching between Turkish–English language pair.

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