A Myanmar (Burmese)-English Named Entity Transliteration Dictionary
Aye Myat Mon†‡, Chenchen Ding†*, Hour Kaing†, Khin Mar Soe‡,
Masao Utiyama†, Eiichiro Sumita†† Advanced Translation Technology Laboratory, ASTREC, NICT

3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan‡ Natural Language Processing Lab., University of Computer Studies, Yangon, Myanmar† {ayemyatmon, chenchen.ding, hour_kaing, mutiyama, eiichiro.sumita}@nict.go.jp‡ {ayemyatmon.ptn, khinmarsoe}@ucsy.edu.mm

Abstract
Transliteration is generally a phonetically based transcription across different writing systems. It is a crucial task for various downstream natural language processing applications. For the Myanmar (Burmese) language, robust automatic transliteration for borrowed
English words is a challenging task because of the complex Myanmar writing system and the lack of data. In this study, we constructed a Myanmar-English named entity dictionary containing more than eighty thousand transliteration instances. The data have beenreleased under a CC BY-NC-SA license. We evaluated the automatic transliteration performance using statistical and neural networkbased approaches based on the prepared data. The neural network model outperformed the statistical model significantly in terms of
the BLEU score on the character level. Different units used in the Myanmar script for processing were also compared and discussed.
Keywords: Myanmar (Burmese), named entity, transliteration, machine translation, neural network

1.

Introduction

Transliteration is the task of transcribing words from asource script to a target script. Generally, the transcription
is phonetically based. Transliteration processing is important for many downstream natural language processing(NLP) tasks, such as machine translation and information
retrieval. In recent years, general sequence-to-sequence
processing techniques for NLP tasks have been developed
significantly. However, a lack of resources is still a problematic issue for many understudied languages.
In this study, we focus on transliteration between Myanmar (Burmese) and English. To facilitate the applicationof data-driven approaches, we manually collected a dictionary containing more than eighty thousand MyanmarEnglish transliteration instances. The data have been released under a CC BY-NC-SA license for research purposes. 1 Based on the dictionary, we conducted experiments on automatic transliteration between Myanmar andEnglish. Specifically, we conducted experiments using
two neural network (NN)-based approaches: the Transformer model using the OpenNMT system 2 (Vaswani etal., 2017; Klein et al., 2017) and a joint agreement bidirectional long short-term memory (LSTM)-based recurrent NN (RNN) using the JANUS3 tool (Liu et al., 2016).
A traditional phrase-based statistical machine translation(PBSMT) system using the Moses4 toolkit (Koehn et al.,2007) was set as a baseline. The experimental results were
evaluated using the BLEU score (Papineni et al. 2002) on
the character level. The experimental approaches performed well on transliteration tasks. The NN-based approaches outperformed the traditional PBSMT by large
gains. The effect of using units at different granularities in
the Myanmar script was also investigated. To the best of
our knowledge, this study is the first systematic work on
the topic of Myanmar-English transliteration driven by arelatively large-scale dataset.
*

The remainder of this paper is organized as follows: In
Section 2, related work is described. In Section 3, issues
for the transliteration of Myanmar are addressed and thecollected dictionary is described. In Section 4, the experimental results are reported, and in Section 5, a discussion
is provided. In Section 6, the paper is concluded and future work is presented.

2.

Related Work

Many Asian languages apply special writing systems, andefforts have been made on transliteration processing for
major languages such as Chinese, Japanese, and Korean(Merhav and Ash, 2018). However, studies are requiredon understudied languages with limited resources.
Generally, the transliteration task can be modeled as asimplified translation task on the character/grapheme level
rather than the word/phrase level, with no (or few) reordering operations. The technical background has been
well established in the field of NLP. A PBSMT system(Koehn et al., 2003) can be used as an off-the-shelf tool
once adequate data are provided. In recent years, NNbased frameworks such as the LSTM-RNN (Cho et al.,2014) have been widely applied to many NLP tasks. The
Transformer model (Vaswani et al., 2017), which introduces a self-attention mechanism, is a state-of-the-art NN
architecture in the NLP field.
Regarding specific studies on transliteration, Finch et al.
(2016) proposed an agreement model of bidirectional
RNN, which outperformed PBSMT on various language
pairs. Wu and Yarowsky (2018) compared several machine translation methods for the transliteration of 591
languages into English. Their conclusion was that a
PBSMT system outperformed other systems including
NN-based approaches. Regarding the case of Myanmar
processing, there are few previous works. Ding et al.
(2017) first attempted a Myanmar name Romanizationtask, where NN-based approaches did not outperform thetraditional approaches of the conditional random field andsupport vector machine. It can be considered that thetransliteration task may be sensitive to the quality andquantity of training data, in addition to the features of thespecific languages involved, so proper approaches should
be investigated case by case.

Corresponding author
http://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/my-en-transliteration.zip
2
http://opennmt.net/OpenNMT-py/
3https://github.com/lemaoliu/Agtarbidir2973
4
https://github.com/moses-smt/mosesdecoder
Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020) , pages 2973–2976
1

Marseille, 11–16 May 2020c European Language Resources Association (ELRA), licensed under CC-BY-NC

3.
3.1

3.3

Transliteration of Myanmar

Overview

The Myanmar language belongs to the Sino-Tibetan language family. It is the official language of the Republic
Union of Myanmar and the native language of the Bamar
people. The Myanmar script is an abugida system. It consists of 33 characters of standalone consonants, four dependent consonants, and tens of diacritic marks that represent vowels and tones. The orthography of Myanmar is
generally syllable-based, although syllables may be
merged in special writing forms. One word can be composed of multiple syllables and one syllable can be composed of multiple characters. Between the characters andsyllables, sub-syllable units may be identified for specifictasks. More details are available in Ding et al. (2017).
The contemporary Myanmar language contains a considerable number of borrowed words from English. Temporarily, there is no consistent standard in Myanmar to transcribe the growing number of borrowed words from or
through English.

3.2

Difficulty

The difficulty in modeling transliteration between Myanmar and English is two-fold. The first issue is the difference in the phonological inventory of the two languages.
Chang (2013) provided a study on the phonology of English loanword adaptation in Myanmar. This study was
based on a list of 280 collected borrowed English words.
As typical features of the Myanmar consonant system,
stops and affricates make a three-way contrast withvoiced, voiceless, and voiceless aspirated; a two-way
voicing contrast is also present with nasals and approximants. These contrasts do not completely exist in English.
Myanmar has a typical seven-vowel system, and the syllable codas are restricted to nasal and glottal. Although
the vowel system of Myanmar is somehow simpler thanthat of English, three tones in Myanmar are superfluous
when transliterating into English. By contrast, consonant
clusters are limited in Myanmar, but abundant in English.
The second issue is non-phonetic orthographies. Both
English and Myanmar apply an orthography that is more
etymologically based than phonetically based. The Myanmar script has relative redundancy regarding its phonology inventory because many phonemes can be realized
in more than one way. Moreover, some special spellings
may be used intentionally to spell borrowed words for an
exotic appearance. English spellings may also cause irregular transliteration where the transcription follows thespelling rather than the real pronunciation.
As a result of the above-mentioned issues, transliteration
between Myanmar and English becomes correspondingly
complex. In the following are some examples. <eu> in“Eurasia” and <u> in “United Kingdom,” although spelt
differently, are transliterated identically as <ယူ> (/jù/) into
Myanmar. This is also the case for some pronunciations
that cannot be distinguished in Myanmar, for example,<aun> in “auntie” and <un> in “uncle” are both transcribed into <အန်> (/ʔàɴ/). By contrast, <j> in “Japan” istranscribed by pronunciation as <ဂျ> (/d͡ʑ.../); however,<j> is more traditionally and consistently transcribed as<ဇ> (/z.../) in the case of “January,” “June,” and “July.”

Dictionary

To collect the transliteration instances, we began with two
English-Myanmar parallel corpora, and then moved toresources on the Internet to enlarge the scale of the data.
Specifically, we used the ALT corpus (Riza et al., 2016;
Ding et al., 2018; Ding et al., 2019; Ding et al., 2020)5
and UCSY corpus (Sin et al., 2018). 6 The ALT corpus
consists of twenty thousand parallel sentences from news
articles and the UCSY corpus contains two hundred thousand parallel sentences collected from different domains,
including local news articles and textbooks. We used the
GIZA++ toolkit7 (Och and Ney, 2003) to obtain the raw
alignments between the source and target language, basedon which the transliteration instances were filtered. From
the Internet, we further collected instances of places, organizations, and person names.
The dictionary was encoded in Unicode. For experimental
investigation, we divided it into three parts by taking thefirst one thousand lines as the test data and the final one
thousand lines as the development data. Statistics for thedata are listed in Table 1, where #Char. denotes Unicodecharacters, #Syl. denotes syllables, and #Sub-Syl. denotes
the sub-syllable units proposed in Ding et al. (2017). Table 2 shows examples of the different units of the Myanmar transcription “ဆွစ်ဇာလန်” for “Switzerland.”

Train
Dev.
Test

#Char.
2,120,773
15,80016,288Myanmar#Sub-Syl.
1,824,597
14,476
14,221#Syl.
1,433,745
10,314
11,201English#Char.
1,884,108
13,604
14,253

Table 1: Statistics for the data.
Char.
Sub-Syl.
Syl.

<ဆ><ွွ><စ><ွ်><ဇ><ွာ><လ><န><ွ်><ဆ><ွွစ်><ဇ><ွာ><လ><န်><ဆွစ်><ဇာ><လန်>

Table 2: Different units in Myanmar processing.

4.

Experiment

The Myanmar data were segmented into different units, as
shown in Table 2, and all English data were lowercased.
Moses was used to train the baseline PBSMT system.
Character alignment was generated using GIZA++ andsymmetrized using the grow-diag-final-and heuristics. A
5-gram language model on character was trained using the
SRILM toolkit (Stolcke, 2002).8 Minimum error rate training (Och, 2003) was used to tune the weight of the features. All other parameters were adapted using the default
setting of Moses. Regarding the JANUS toolkit, which
implements a bidirectional agreement LSTM-RNN model,
the hyperparameters followed the original paper (Liu et5

http://www2.nict.go.jp/astrecatt/member/mutiyama/ALT/index.html
6
http://lotus.kuee.kyoto-u.ac.jp/WAT/my-en-data/7
http://www.statmt.org/moses/giza/GIZA++.html
8
http://www.speech.sri.com/projects/srilm/

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al., 2016). Specifically, the settings were 500 for embedding, 500 for the hidden unit dimensions, and 16 for thebatch size. AdaDelta was used as the optimizer, with adecay rate of 0.95 and ε of 10-6. The model was trainedwith 100 epochs. Regarding the Transformer model using
the OpenNMT toolkit. The hyperparameters used in theexperiments are listed in Table 3.

calculation. The experimental results for English-toMyanmar (En→My) transliteration in addition to thereversed Myanmar-to-English (My→En) transcription areprovided in Tables 4 and 5, respectively. For the Transformer model, different combinations of layers (L) andheads (H) were compared in the experiments.

5.
Parameter
rnn_size
word_vec_size
transformer_ff
encoder_type
decoder_type
position_encoding
train_steps
max_generator_batches
Dropout
batch_sizebatch_type
normalization
accum_countOptim
adam_beta2
decay_method
warmup_steps
learning_rate
max_grad_norm
param_init
param_init_glorot
label_smoothingvalid_steps
save_checkpoint_steps
world_size
gpu_ranks

Setting
512
512
2048
Transformer
Transformer
50000
2
0.1
1024
tokenstokens
2
adam
0.998
noam
8000
2
0
0
0.1
10000
10000
1
0

In this section, we discuss case studies on transliteration
instances that were difficult to process. In Tables 6 and 7,
the outputs of different systems are compared for identicaltransliteration instances in both directions.
Unit

PBSMT

LSTM-RNN

Transformer(L2, H8)

Transformer(L4, H8)

Char.
0.82
0.87
0.92
0.89
0.91
0.92
0.91

Sub-Syl.
0.81
0.90
0.92
0.92
0.92
0.93
0.92

Char.
0.73
0.84
0.75
0.85
0.86
0.86
0.87

Sub-Syl.
0.64
0.84
0.74
0.86
0.84
0.80
0.76

Syl.
0.84
0.92
0.92
0.92
0.91
0.93
0.92

Char.

ကာဒီဖ်

cardift

Sub-Syl.

ကာဒီအက်ဖ်

cardifSyl.

ကာဒီအက်ဖ်အက်ဖ်

carဒစ်ဖ်

Char.

ကာဒီဖ်

kadif

Sub-Syl.

က ားဒစ်ဖ်

kadif

Syl.

ကာဒစ်

kadif

Char.

က ဒစ်ဖ်

cardiff

Sub-Syl.

ကာဒစ်

cardif

Syl.

ကာဒစ်

cars

Char.

က ဒစ်ဖ်

cardif

Sub-Syl.

ကာဒစ်

cardiff

Syl.

ကာဒစ်

carbock

The spelling of <စ်ဖ်> used to transcribe <iff> is not permitted in Myanmar native words. It can be observed thatsyllable-based processing cannot handle such exceptional
structures, regardless of which system is used.
Unit

Syl.
0.77
0.76
0.76
0.78
0.76
0.85
0.86

PBSMT

LSTM-RNN

Table 5: English-to-Myanmar results.

Transformer(All)

The BLEU score (Papineni et al. 2002) on the character
level was used in the evaluation. We used bleukit9 for the
9

My→En

Some non-native Myanmar spellings may appear in thetranscription of borrowed English words. Table 6 presentsa typical example of the pair “cardiff” and “က ားဒစ်ဖ်.”

Table 4: Myanmar-to-English results.
PBSMT
LSTM-RNN
Transformer (L6, H8)
Transformer (L2, H2)
Transformer (L4, H4)
Transformer (L2, H8)
Transformer (L4, H8)

En→My

Table 6: Results for “cardiff” ↔ “ကာားဒစ်ဖ်”.

Table 3: Hyperparameter settings for the Transformer
model.

PBSMT
LSTM-RNN
Transformer (L6, H8)
Transformer (L2, H2)
Transformer (L4, H4)
Transformer (L2, H8)
Transformer (L4, H8)

Discussion

En→My

My→En

Char.

ဒဂျ ားကဗို စ်jokovic

Sub-Syl.

ဒီဂျ ကဗို စ်jokovic

Syl.

ဂဂျာ်ကိုဗစ်

jokovic

Char.

ဒီဂျ ကဗို စ်jokovic

Sub-Syl.

ဒီဂျ ကဗို စ်jokovic

Syl.

ဒီဂျ ားကဗို စ်jokovic

Char.

ဒီဂျ ကဗို စ်jokovic

Sub-Syl.

ဒီဂျ ကဗို စ်jokovic

Syl.

ဒီဂျ ကဗို စ်jokovic

Table 7: Results for “djokovic” ↔ “ဂျ ကိုဗစ်.”http://www.nlp.mibel.cs.tsukuba.ac.jp/bleu_kit/

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Table 7 presents a difficult example of the pair “djokovic”
and “ဂျ ကိုဗစ်,” for which no system provided the correct
results for either the En→My or My→En direction. The
word “djokovic” itself is not a native English word, so all
systems provided the more common spelling of “jokovic”
in My→En processing. <djo> also caused difficulty in
En→My processing, and in most cases, <d> was transcribed separately as <ဒ...>. The most approximate result
was provided by PBSMT using Myanmar syllables, where<dj> was correctly transcribed as <ဂျ...>; however, thevowel was not transcribed exactly.
As the best performances of different Myanmar units do
not differ so obviously in Tables 4 and 5, we consider thatusing character or sub-syllable units in Myanmar is abetter option than syllables to avoid errors caused by irregular spellings, in most cases.

6.

Conclusion and Future WorkIn this study, we built and released a dictionary of Myanmar-English transliteration instances. Experiments wereconducted on a baseline PBSMT system and two NNbased approaches to provide benchmark performancebased on the prepared data.
Temporarily, we are collecting more instances so that thescale of the dictionary exceeds one hundred thousand
instances. We will investigate the effect of using the data
to improve the performance of Myanmar-English translation in the near future.

7.

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