Burmese (Myanmar) Name Romanization:
A Sub-syllabic Segmentation Scheme for
Statistical Solutions
Chenchen Ding1(B) , Win Pa Pa2 , Masao Utiyama1 , and Eiichiro Sumita1
1

Advanced Translation Technology Laboratory, ASTREC,
National Institute of Information and Communications Technology,
3-5 Hikaridai, Seikacho, Sorakugun, Kyoto 619-0289, Japan{chenchen.ding,mutiyamam,eiichiro.sumita}@nict.go.jp
2
Natural Language Processing Lab, University of Computer Studies,
Yangon, Myanmar
winpapa@ucsy.edu.mm

Abstract. We focus on Burmese name Romanization, a critical task
in the translation of Burmese into languages using Latin script. As
Burmese is under researched and not well resourced, we collected andmanually annotated 2, 335 Romanization instances to enable statistical
approaches. The annotation includes string segmentation and alignment
between Burmese and Latin scripts. Although previous studies regard
syllables as unbreakable units when processing Burmese, in this study,
Burmese strings are segmented into well-designed sub-syllabic units toachieve precise and consistent alignment with Latin script. The experiments show that sub-syllabic units are better units than syllables forstatistical approaches in Burmese name Romanization. The annotateddata and segmentation program have been released under a CC BY-NC-SA
license.

1

Introduction

Linguistically, Romanization is the task of transforming a non-Latin writing system into Latin script. It is a language-speciﬁc task in natural language processing(NLP), and an important module in a statistical machine translation (SMT) system, required to handle the unknown proper nouns that occur when translating
a language using non-Latin script into languages using Latin script.
The task of Burmese (Myanmar) name Romanization is investigated in thisstudy. Burmese is an understudied language and there are not many resources
available for its study, although research on Burmese begun in recent years.
Current research on Burmese focuses on applying available techniques to performbasic tasks in NLP such as tokenization [5], parsing [4], and SMT [3], as well as toperform basic tasks in speech processing, such as automatic speech recognition[10] and speech synthesis [17].
This study thus proceeds from raw Romanization instance collection to manual annotation and ﬁnally empirical investigation. The annotation was conductedc Springer Nature Singapore Pte Ltd. 2018

K. Hasida and W. P. Pa (Eds.): PACLING 2017, CCIS 781, pp. 191–202, 2018.
https://doi.org/10.1007/978-981-10-8438-6_16

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considering Burmese phonology and phonotactics, as well as the conventional
Romanization spellings. Statistically based experiments were then performed on
the annotated data. The contribution of this study is that well-designed subsyllabic units rather than integrated syllables in Burmese, are regarded as segments and proved to be eﬃcient representations through experimental results.
Compared with syllables, these sub-syllabic units, although they need a speciﬁc process to extract, reduce the number of segment types, which can signiﬁcantly reduce the sparseness for statistical approaches. The annotated data anda python implementation of the sub-syllabic segmentation scheme have beenreleased for NLP community under a CC BY-NC-SA license.1

2

Related WorkThe Romanization task is a string-to-string transformation that can be cast asa simpliﬁed translation task working on grapheme level rather than word (orphrase) level with no (or few) reordering operations. Thus, general SMT techniques can be facilitated once training data are available. Phrase-based SMT
plays an important role in Romanization tasks and has been taken as a baseline system in recent workshops [1,2], whereas recent neural network techniques
provide further gains in performance [6,7].
Although there are several available transcription guidelines for Burmese
Romanization,2 conventional and inconsistent spellings are more prevalent inpractice use. To the best of our knowledge, there is still no systematic researchon this speciﬁc topic in NLP. Related studies may include the grapheme-tophoneme (G2P) study on Burmese [16], which is a task for speech processing
rather than NLP, and thus relatively large units in utterance, i.e., syllables, wereused in this research. Compared with G2P, the Romanization task further suﬀers
from the diversity of conventional spellings. For example, a common BurmeseĂ
rhyme /-ið £/ may have four equal variants -in, -inn, -yn, and -ynn in practice.
Non-phonological spellings are also common, such as representation of a creaky
tone by an ending t or using English-like spellings for similar phonemes, e.g.,Ă-ike for /-aiP/, and -oo for /-u £/.
As a primary contribution of this study, we provide a solid basis for the taskof Burmese name Romanization, for the ﬁrst time, that includes all components
from data to practical techniques. We use sub-syllabic units, speciﬁcally, onset,
rhyme, and tone, and experimentally prove that they are more ﬂexible and precise units than syllables for statistical approaches. In a wider sense, as the study
is based on an insightful consideration of Burmese phonology and phonotactics,
we believe the sub-syllabic units are also applicable for other related Burmese
processing tasks.
1
2

http://www.nlpresearch-ucsy.edu.mm/NLP UCSY/name-db.html.
Typical ones are the Myanmar Language Commission Transcription System, theLibrary of Congress’ ALA-LC Romanization index system for Burmese (http://
www.loc.gov/catdir/cpso/romanization/burmese.pdf), and the Okell’s system [13].

Burmese (Myanmar) Name Romanization

3

Burmese Syllable

3.1

Syllable Composition

193

Burmese applies an abugida writing system, i.e., consonant letters stand for a syllable with an implicit inherit vowel, and diacritics modify consonant letters to change/depress the inherent
vowel, to form consonant clusters, or to change tones. A Burmese
syllable is illustrated in the beginning of the section, where thenumbers indicate the order of the characters in the composition.
1 and 4 are consonant letters, and 2 , 3 , 5 , and 6 are diacritics. 2 and3 form consonant clusters with 1 . 5 is a tone mark. 6 is the virama todepress the inherent vowel of 4 to form the coda of the syllable. As illustrated,
Burmese syllables are usually composed of multiple characters, while syllables
can be identiﬁed by rules, i.e., all diacritics and consonant letters with a virama(inherent vowel depressor) must be attached to the letter they modify [5].
3.2

Syllable-Based Processing and Limitation

Syllables are usually treated as basic atoms, i.e., unbreakable units, in related
NLP studies such as Burmese word segmentation [5] or SMT [3]. In these studies, the syllable identiﬁcation is the ﬁrst step before any further processing.
This treatment is proper regarding to the nature of those morphology or syntax
related NLP tasks, as structures and fragments within syllables generally haveno signiﬁcant meaning attributing to sentence-level analysis.
Syllable-based processing is also a straightforward way in the Romanization
task. However, the structure within syllables have a clearer and more importantrole in the task, because Romanization is a task more related to phonologicaland phonotactic features. On the other hand, the syllable turns to be a too
integrated unit for detailed analysis and modeling in terms of phonology, where
diﬀerent parts within a syllable should be reviewed. To solve the Romanization
task more eﬃciently, we step into the syllable structure and extract sub-syllabic
segments, to achieve a precise and consistent correspondence between Burmese
and Latin scripts by establishing a monotonic and one-to-one alignment.
An intuitive example on the proposed sub-syllabic segmentation is illustrated
in Fig. 1, with the comparison of syllables and characters. As illustrated, syllables
are integrated units but with relatively complex internal structures, i.e., syllables are too large segments in processing; character-level analysis, oppositely,
turns too detailed that the correspondence between the two writing systems
are diﬃcult to sort out, i.e., characters are too tiny segments. In practice, thesyllable-based processing may cause serious sparseness in model learning due tothe large amount of diﬀerent types, and the character-based processing introducing excessive and tricky alignment. The proposed sub-syllabic segmentationis thus at a good balance point of integrity and preciseness between the two
extremes in handling the Romanization task.

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Fig. 1. Overview of sub-syllabic segmentation. Upper-left is a raw Romanization
instance; upper-right shows syllables. In the sub-syllabic segmentation, @/. show
inserted/silent segments. The character-level alignment is complex, where dash lines
show the spellings aﬀected by surrounding Burmese characters. Boundaries of syllables and sub-syllabic units are illustrated by solid and dash vertical bars, resp., in thecharacter-based analysis.

As syllables are integrated units in the Burmese script, the segmentation
scheme requires further transposition and insertion processing. The descriptionsof segmentation and the transposition/insertion processing are given in the following subsections respectively.

4

Sub-syllabic Segmentation

4.1

Onset-Rhyme-Tone Segmentation

Figure 2 is a diagram of the overall sub-syllabic segmentation scheme, where
the three gray blocks are the sub-syllabic units designed in this study. It is relatively intuitive to divide a Burmese syllable into two segments, onset and rhyme,
despite the diﬃculties introduced by medial consonants, where multigraphs of
Latin letters may be used for Burmese letter clusters (Fig. 3). The rhyme is notfurther dividable except to stripe tones annotated by explicit marks.
Speciﬁcally, four diacritics are used to represent the medial consonants, i.e.,y apin, y ayit, w ahswe, and hahto to represent /-j-/, /-j-/,3 /-w-/, and /-h -/,4
respectively. As shown in Fig. 2, yapin, yayit, and hahto are placed into the onset
3

Yayit originally represents /-r-/ while in the modern standard Burmese the phoneme/r/ has been merged into /j/.
4
Actually a voiceless sign, e.g., changing /n-/ to /n-/.
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Fig. 2. Proposed sub-syllabic segmentation.

segment, but wahswe into the rhyme segment. This scheme is adopted for two
reasons. (1) The phonotactical constraints are strict on yapin, yayit, and hahto,
but loose on wahswe. Hahto is only used on sonorants (nasals and approximants)
and yapin/yayit only on velars and bilabials.5 In contrast, wahswe can be freely
combined with all consonant phonemes with the trivial exception of /w-/.6 (2)Yapin, yayit, and hahto may change the property of the initial consonants while
wahswe may change the property of the nucleus vowels. Besides the voicelessmarker of hahto, yapin/yayit palatalize velar consonants.7 Contrarily, wahswe
may add a rounded feature to the following nucleus vowels.8 These two issues
aﬀect conventional Romanization spelling. As shown in Fig. 3, multigraphs of
Latin letters are used to transcribe onset clusters with yapin, yayit, and hahto,
or wahswe-rhyme clusters. Consequently, our scheme is the only feasible way
to segment Burmese onset and rhyme to achieve a monotonic and one-to-onealignment with Latin script in conventional Romanization.
Burmese has two codas, i.e., nasal and glottal, and three tones, i.e., creaky,
low, and high. The three tones can be combined freely for open and nasal-ended
syllables, but not for glottal ended ones. The glottal ending thus may be regarded
as a fourth tone in some analysis. Further, the nasal ending can be regarded asa nasalization of nucleus vowels. Hence, the coda is not a necessary component
from an extreme viewpoint, which places the nasal ending into nuclei and theglottal into tones. However, in the writing system, the nasal and glottal endings
are represented by consonant letters with a virama. These “coda-letters” aﬀect
nucleus vowels, so that they are not completely detachable. As a result, only twotone marks, i.e., the visarga (high) and aukmyit (creaky) are segmented in ourscheme, as they are “pure” tone marks,9 and any further segmentation would
ruin the integration of the rhyme.

5

Yapin can also be combined with /l-/.
/Pw-/ may be argued in some references. The combination appears marginally inborrowing words and interjections.
7E.g., /kj-/ is actually /c-/ or /tC-/.
8E.g., changing /-aP/ to /-UP/ and changing /-að/ to /-Uð/.
9
The visarga is usually not transcribed and aukmyit is inconsistently represented bya ﬁnal t in Romanization.
6

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Fig. 3. Examples of common Latin multigraphs used for Burmese letter clusters.

4.2

Transposition and Insertion

Although the sub-syllable units have been ﬁgured out in the previous subsections, a raw Burmese string requires pre-processing for the segmentation, due
to the coding order of Burmese script. Examples of segmentation with transposition and insertion are shown in Figs. 4 and 5, respectively.
For the onset-rhyme segmentation, an onset segment with multiple components is coded in the order of “C + yapin/yayit + wahswe + hahto,” where C is
the initial consonant.10 It is obvious that the segmentation cannot be conductedunder the coding order once wahswe and hahto appear simultaneously. Hence, awahswe-hahto swapping is required. For the rhyme-tone segmentation, one
problem is the order of the aukmyit and virama in nasal-ended creaky-toned
syllables.11 As the standard order should be “aukmyit + virama” in coding,12an aukmyit-virama swapping is required.
In addition to the transposition pre-processing, a “dummy rhyme” is inserted
for all the “bare onsets”, i.e., syllables without an explicit rhyme where theimplicit inherent vowel performs the role of the nucleus. This insertion postprocessing can provide a precise alignment between Burmese and Latin scripts,
as the vowels are always explicit in an alphabetic system but may be implicit in
an abigida system. Note that the example in Fig. 4 and the ﬁnal example (mwa)in Fig. 5 do not require this insertion because the rhyme segments are not empty,
i.e., “coda-letter” and wahswe can perform the role of the rhyme segment.

5

Annotated Data

Our Burmese Romanization instances were ﬁrst collected from students andfaculty names in a university. More instances were then collected from the Internet, including names of public ﬁgures and names from minority nationalities in
Myanmar.
To process the raw instances, we ﬁrst segmented Burmese and Latin strings
roughly into consonant and vowel segments and used a greedy algorithm to
10

Multiple medial consonants for one initial consonant is possible while yapin and yayit
cannot appear simultaneously.
11
As mentioned, glottal endings take no tones.
12
However, the swapped order may introduce no problem in displaying, so both orders
are used in daily typing.

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Fig. 4. Transposition before segmentation.

Fig. 5. Insertion for onsets without a rhyme.

generate a preliminary alignment with the help of an aligner.13 Around 60% of
the instances were aligned consistently in this stage, and we manually checked theerrors to reﬁne the overall segmentation scheme. In the second round, we ﬁlteredout questionable instances by cross-checking. Errors were found and cleaned inaround 10% of the instances. In the third round, more tolerable instances, i.e.,
Burmese names with more than one acceptable Romanization, were added. We
obtained a ﬁnal total of 2, 335 annotated instances.
Speciﬁc annotation samples are illustrated in the appendix attached to thispaper.

6

Experiment

In recent research, direct sequence-to-sequence approaches launched by neuralnetwork techniques have been widely applied in various NLP tasks. We experiment a state-of-the-art long short-term memory (LSTM) based recurrent neuralnetwork (RNN) approach with a bidirectional search in decoding14 [9]. The approach performs well on diﬀerent transliteration tasks. We also test two standard
13
14

Using GIZA++ [12] at http://www.statmt.org/moses/giza/GIZA++.html.
An open-sourced tool is available at https://github.com/lemaoliu/Agtarbidir.

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machine learning approaches, conditional random ﬁelds (CRF) and support vector machine (SVM), by handling the task in a sequence labeling manner. We use
the CRF++ toolkit15 [8,15] and the KyTea toolkit16 [11] for CRF and SVM
experiments, respectively.
All the experiments were cross-validated. The RNN handles the task in asequence-to-sequence way on character-level, without using any a priori knowledge,17 while CRF/SVM take advantage of the designed segmentation and manual alignment. The features for CRF/SVM were segments up to tri-grams. Specifically, the -charn and the -charw options are set to three for KyTea. The features used for CRF++ are Cnn+k (k ∈ [1, 2], n ∈ [−k, 0]) for segment sequences
and Cn (n ∈ [−2, 2]) for single segments. The settings from the original paper
were used for the RNN: embedding size is 500, hidden unit dimension is 500,
and batch size is 4. AdaDelta is used for optimization with a decay rate ρ of 0.95
and an  of 10−6 . We evaluated the experimental results using two metrics: theaccuracy of target segments (SEG),18 and the accuracy of target strings (STR)
where the BLEU score [14] at Latin letter level was applied.
The generalized LSTM-based RNN approach cannot handle the task well on
our data. The STR is around .72 in 8-fold cross validation. The RNN actually generates “Burmese-styled” Latin transcriptions but inaccurate. We thus consider
the performance to be reasonable and attribute the causes to (1) the data size,
which is insuﬃcient to support an RNN model, and (2) the intrinsically complex
mapping between the two writing systems, where character-level many-to-manyalignment is common and is diﬃcult to model.
The CRF/SVM results using sub-syllabic units are listed in Table 1. The
performance of the two approaches are similar: SEG is around .95 and STR isaround .91. These results are acceptable, but there is still room for improvement.
For comparison, Table 2 shows the results using syllables.19 As the segments are
diﬀerent, the SEG of the two tables cannot be compared directly. However, STR in
Table 2 is lower than in Table 1, with statistical signiﬁcance at the p < 1% level.
The main cause of this diﬀerence is the sparseness, which is evidenced by thepercentage of unknown segments (UNK) in the tables. Speciﬁcally, there are 548
types of syllables in our data, while the sub-syllabic segmentation reduces thenumber of types to 136, which largely reduces the sparseness. Consequently, we
conclude that the sub-syllabic units designed in this study provide an eﬃcientinterface for machine learning approaches to handle the Romanization task.

15

http://taku910.github.io/crfpp/.
http://www.phontron.com/kytea/.
17
I.e., on the level in the bottom rank in Fig. 1, with no explicit alignment or unit
boundaries between characters.
18
SEG cannot be applied to the RNN approach as the alignment and segmentation are
not explicit variables.
19
I.e., the results in Tables 1 and 2 are based on the middle and upper-right parts in
Fig. 1, respectively.
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Table 1. CRF/SVM results on sub-syllabic units.
2-fold4-fold8-foldSEG .946/.944 .948/.947 .947/.947
STR .912/.907 .913/.911 .913/.910
UNK 0.13%

0.10%

0.09%

Table 2. CRF/SVM results on syllables.
2-fold4-fold8-foldSEG .877/.882 .886/.888 .887/.889
STR .878/.887 .888/.893 .890/.894
UNK 3.01%

7

2.48%

2.31%

Conclusion and Future WorkIn this study, we focused on the Burmese name Romanization task, from the
preparation of deliberately segmented and aligned data to experiments and discussion of statistical approaches. We are collecting more Burmese Romanization
instances including the names of places and organizations. The annotated dataand segmentation program have been released under a CC BY-NC-SA license topromote the research of NLP on Burmese.

Appendix
Figure 6 shows speciﬁc annotation instances for a further illustration and demonstration. The data are organized in a three-section format of– original Burmese name,– original Romanization, and– aligned Burmese/Latin graphemes,
separated by |||.
The descriptions of speciﬁc instances are as follows.
I. An ordinary Romanization instance.
II. A Burmese name with a western expression (Grace) as a component. Generally, such western expressions are segmented according to the Burmese
spellings. In this instance, Grace is segmented into /G /@ /r /a /@ /ce.
Notice that we just apply the same @ for the dummy vowel on Burmese sideand for the silent placeholder on Latin side, which causes no confusion.
III. A Burmese name derived from Pali (Wanna),20 where stacked consonants
appear (/n /n). The stacked consonants are split and aligned to separate
20

The Romanization instance is directly taken from the released data set. A morecommon Romanization of the Pali-derived name is Wunna.

Fig. 6. Speciﬁc annotation instances on Burmese name Romanization.

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Latin letters. If no doubled Latin letters are used, the second Burmese
character will be simply aligned to a silent placeholder @. The stacking
operator is always aligned to @.
IV. A Burmese name with complex stacking, that the rhyme of the previous
syllable (/in) is stacked with the following onset (/gy).
V. A Burmese name with more complex stacking, that part of the rhyme ofthe previous syllable (/ein) is stacked with the following onset (/g), which
is taking a further vowel diacritic (/i). The instances IV. and V. illustrate
the necessity on the segmentation of stacked characters.
VI. A Burmese name with stacked consonants, for which two syllables are kept
as one word (Thinzar) in Romanization.
VII. A Burmese name with stacked consonants, for which two syllables are separated as two words (Thin Zar) in Romanization. Notice the Burmese names
in instance VI. and VII. are identical. They are treated as two diﬀerent
Romanization instances due to the spellings in Romanization are diﬀerent.

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