言語処理学会 第23回年次大会 発表論文集 (2017年3月)

Statistical Romanization for Abugida Scripts:
Data and Experiment on Khmer and Burmese
Chenchen Ding1 Vichet Chea2 Win Pa Pa3 Masao Utiyama1 Eiichiro Sumita1
1
ASTREC, NICT, Japan 2 NIPTICT, Cambodia 3 UCSY, Myanmar
11{chenchen.ding, mutiyama, eiichiro.sumita}@nict.go.jp
2vichet.chea@niptict.edu.kh 3 winpapa@ucsy.edu.mm

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). Despite standard romanization systems, conventional transcription methods are applied prevalently in many languages. The spellings
are thus inconsistent and complicated in some cases.
Consequently, statistical approaches are required for
an efficient solution on this task.
We focus on the name romanization for languages
using abugida scripts. Different from an alphabetic system, in an abugida system, consonant lettersstand for a syllable with an implicit inherent vowel,and diacritics modify consonant letters to change ordepress the inherent vowel. We designed annotation
schemes to establish a precise, consistent, and monotonic correspondence between the two different writing systems on grapheme level, through which various machine learning approaches are facilitated.
Specifically, we collect and manually align 7, 658
and 2, 335 person name romanization instances for
Khmer and Burmese (Myanmar), respectively. Bothlanguages are with limited resources and NLP studies. Experimental results demonstrate that standard
approaches of conditional random fields (CRF) andsupport vector machine (SVM) supervised by themanually annotated data achieve high precision inromanization. The annotated data have been released under a CC-BY-NC-SA license.1

2

Related WorkIn engineering practice, the romanization task is astring-to-string transformation, which can be cast asa simplified translation task working on grapheme
1 The Khmer data are available at http://niptict.
edu.kh/khmer-name-romanization-with-alignment-ongrapheme-level/. The Burmese data are available at http://www.nlpresearch-ucsy.edu.mm/NLP_UCSY/name-db.html.
Please contact the first author to obtain the data if theservers are unstable.

level rather than on word (or phrase) level with no(or few) reordering operations. Hence, general SMT
techniques can be facilitated once training data areprepared. The phrase-based SMT plays a role ofbaseline in recent workshops [1, 2], whereas neuralnetwork techniques provide further gains in performance [4, 5]. Although neural network-based, purestring-to-string approaches prove powerful on different transliteration tasks, there is still room forimprovement, especially on tasks between different
writing systems. For example, the Thai-to-English
task, which is similar to our task, has relatively poor
performance in NEWS 2015. The problem around
diacritics in abugida is also stated in Kunchukuttan
and Bhattacharyya [6]. General techniques may offer
acceptable solutions overall, but specialized investigation and processing are required for further improvement on tasks for specific languages. In the remainder of the paper, the Khmer and Burmese datawith manual annotation are described in Secs. 3 and4, respectively. Experiments are reported in Sec. 5,and Sec. 6 contains the conclusion and future work.

3Khmer Data

We collect and annotate 7, 658 real Khmer name romanization instances. An example of a Khmer namealigned with its romanization is illustrated in Fig. 1.
A special feature of Khmer script is that there are
two series of consonant letters, which have different
inherent vowels. Furthermore, diacritics represent
different vowels when added to corresponding consonant series, which leads to a very complicated vowel
system. Another feature of Khmer script is that thevertically stacked consonant letters are very common. One reason is the abundant consonant clusters in phonology; another reason is the etymological spelling of Sanskrit (Pali) derived words. Thosestacked letters are not strictly based on the syllablestructure. Additionally (and more problematically),
the virama, i.e., the diacritic used to suppress the inherent vowel, is absent in Khmer script, which makes
the identification of onset and coda difficult.

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13106

កូ យ ចន្ទ្រា

KOY CHANTRA

25

ក ូូ យ .

ច . ន ូ ទ ូ រ ូ

1

23K

O

Y4.

a character-by-character alignment can be established.3 According to our overall principles, Khmer
consonant letters are aligned to Latin consonantgraphemes; diacritics, including inserted inherentvowels, are aligned to Latin vowel graphemes; andany silent part is aligned to a placeholder.

118567891011CH AN.

T.

RA

4

We collect and annotate 2, 335 real
Burmese name romanization in4
stances, including students and faculty names in a university, public
figure names and names from mi5
32
nority nationalities in Myanmar.
Syllable are clear and integrated units in Burmese
script, which can be identified by rules, i.e., all diacritics and consonant letters with a virama must beattached to the letter they modify [3]. As an example, a Burmese syllable composed of six characters
is illustrated in the beginning of the section, where
the numbers indicate the order of the characters inthe composition. 1 and 4 are consonant letters,and 2 , 3 , 5 , and 6 are diacritics. Specifically,
2 and 3 form consonant clusters with 1 , 5 is atone mark, and 6 is the virama to depress the inherent vowel of 4 to form the coda of the syllable.
In order to provide an efficient interface for theRomanization task, we step into the syllable structure and further extract sub-syllabic segments, , i.e.,onset, rhyme, and tone, to achieve a precise and consistent correspondence between Burmese and Latin
scripts. Fig. 2 is a diagram of the sub-syllabic segmentation scheme. It is relatively intuitive to divide
a Burmese syllable into two parts, onset and rhyme,
despite the difficulties introduced by medial consonants. The rhyme is not further dividable except into stripe tones annotated by explicit marks.
Specifically, four diacritics are used to represent
the medial consonants, i.e., yapin, yayit, wahswe,and hahto to represent /-j-/, /-j-/,4 /-w-/, and /-h -/,5
respectively. As shown in Fig. 2, yapin, yayit, andhahto are placed into the onset part, but wahswe
into the rhyme part. This scheme is adopted for
two reasons. (1) The phonotactical constraints arestrict on yapin, yayit, and hahto, but loose on wahswe. Hahto is only used on sonorants (nasals andapproximants) and yapin / yayit only on velars andbilabials.6 In contrast, wahswe can be freely combined with all consonant phonemes with the triv-လွှင ့်11

Figure 1: A Khmer name composed of two words.
The upper part is the raw string pair and the lower
part is the grapheme-aligned pair. In the raw Khmer
string, the bold lines show the boundary of the unbreakable unit in writing and the thin lines distinguish the components. The order of Khmer letters
is marked by numbers. 4 is the space; 7 and 9
are stacking operators. The “.” stands for inserted
inherent vowels on the Khmer side and for a silent
placeholder on the Latin side.
As phonemic syllables and writing units do notmatch well in Khmer script. Consistent alignment
principles are thus difficult to establish if the syllables or writing units are taken as atoms in processing.
Furthermore, there are numerous types of syllables
and writing units, which are too complex for statistical model learning because of sparseness. Basedon these facts, we prefer a pure character-level alignment, where standalone consonant letters, diacritics,
and the invisible staking operator are separated andtreated equally as grapheme on the Khmer side.
A consequent problem is the inherent vowels, once
we completely ignore the syllable structure. We cannot judge whether a standalone consonant stands
for only one consonant or contains a further inherent vowel, due to ambiguity in the writing system.
We thus apply a scheme to insert a mark to represent the inherent vowel for all the “bare” consonant
letters (i.e., consonant letters without any diacritics or stacking operators) to establish a consistentalignment. This insertion is thus decisive based on
the surface spelling, and the ambiguity on the presence or absence of the inherent vowel is converted towhether the inserted mark is silent.
In the example of Fig. 1. the consonant lettersare 1 , 3 , 5 , 6 , 8 , and 10 , where 3 and 5
are bare consonant letters,2 after which the inherent vowel is inserted (noted by a dot here and indicated by a wedge without original index). Then
2 2Burmese Dataand 11 are diacritics for 1 and 10 , respectively,

and 6 and 8 are followed by staking operators. So thesefour consonant letters are not bare.

26645

3 As the task is to transform Khmer script to Latin script,
the graphemes are not guaranteed to be single letters on theromanization side, e.g., the 5 corresponds to CH here.
4 Yayit originally represents /-r-/ while in the modern standard Burmese the phoneme /r/ has been merged into /j/.
5 actually a voiceless sign, e.g., changing /n-/ to /n-/.
˚
6 Yapin can also be combined with /l-/.

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syllable

onset
initialmedialwahswe-hahto aukmyit-virama
swapping
swapping

rhyme
nucleuscoda*လွှင ့်tone= လ + ွ + ွ + င + ွ + ွ့်hlwint-j-, -r-*, -h-, -w-

Figure 2: Burmese sub-syllabic segmentation.

law

hngat

လွွငွ့်

lahw

ngat

rhyme

toneonset
7

ial exception of /w-/. (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 voiceless marker of hahto,
yapin / yayit palatalize velar consonants.8 Contrarily, wahswe may add a rounded feature to the following nucleus vowels.9 The two issues affect conventional romanization spelling that multigraphs of
Latin letters are used to transcribe onset clusters
with yapin, yayit, and hahto, or wahswe-rhyme clusters. So, the scheme is the only feasible way to segment Burmese onset and rhyme to achieve a monotonic and one-to-one and alignment with Latin script
in conventional romanization.
Burmese has two codas, nasal and glottal, andthree tones, 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 someanalysis. Further, the nasal ending can be regarded
as a nasalization of nucleus vowels. Hence, the coda
is not a necessary component from an extreme viewpoint, which places the nasal ending into nuclei andthe glottal into tones. However, in the writing system, the nasal and glottal endings are represented
by consonant letters with a virama. These “codaletters” affect nucleus vowels, so that they are notcompletely detachable. As a result, only two tonemarks, i.e., the visarga (high) and aukmyit (creaky)
are segmented in our scheme, as they are “pure” tonemarks,10 and any further segmentation would ruin
the integrated of the rhyme.
Specific transposition is required for the subsyllabic segmentation. An examples is illustrated inFig. 3. For the onset-rhyme segmentation, an onset
part with multiple components is coded in the orderof “C + yapin / yayit + wahswe + hahto”, where C
is the initial consonant.11 It is obvious that thesegmentation cannot be conducted under the coding order once wahswe and hahto appear simultane7 /Pw-/ may be argued in some references. The combination
appears marginally in borrowing words and interjections.
8 e.g., /kj-/ is actually /c-/ or /tC-/
9 e.g., changing /-aP/ to /-UP/ and changing /-að/ to /-Uð/10 The visarga is usually not transcribed and aukmyit is inconsistently represented by a final t in Romanization.
11 Multiple medial consonants for one initial consonant ispossible while yapin and yayit cannot appear simultaneously.

sub-syllabicsegmentation

လhl+

ွင့်+winွွtFigure 3: Transposition before segmentation.

ously. Hence, a wahswe-hahto swapping is required. For the rhyme-tone segmentation, one problem is the order of the aukmyit and virama in nasalended creaky-toned syllables.12 As the standard order should be “aukmyit + virama” in coding,13 an
aukmyit-virama swapping is required.
In addition to the transposition pre-processing, a“dummy rhyme” is inserted for all the “bare onsets”,
identical to the insertion process applied on Khmer.

5Experiment

We tested a state-of-the-art bi-directional LSTMbased RNN approach [8],14 as well as standard
CRF15 and SVM16 approaches on our data.
The experiments were cross-validated. The RNN
handles the task in a sequence-to-sequence way on
character-level, without using any a priori knowledge, while CRF and SVM take advantage of thedesigned segmentation and manual alignment. Thefeatures for CRF and SVM were tokens up to trigrams. The settings from the original paper wereused for the RNN.17 We evaluated the experimental
results by two metrics: the accuracy of target token
labeling (TOK)18 and the accuracy of target strings(LAT) where BLEU [10] on Latin letters was used.
The experiments by LSTM-based RNN are conducted using an eight-fold cross-validation on ourdata without using manual alignment. The performance reaches .953 in terms of LAT on our Khmer
12 As mentioned, glottal endings take no tones.
13 However, the swapped order may introduce no problem indisplaying, so both orders are used in daily typing.
14 https://github.com/lemaoliu/Agtarbidir
15 CRF++ [7, 11] at http://taku910.github.io/crfpp/
16 KyTea [9] at http://www.phontron.com/kytea/
17 Embedding size is 500, hidden unit dimension is 500, andbatch size is 4. AdaDelta is used for optimization with a decayrate ρ of 0.95 and an  of 10−6 .
18 TOK cannot be applied to the RNN approach as the alignment is not an explicit variable.

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TOKLAT

2-fold.987 / .988
.974 / .977

4-fold.988 / .989
.976 / .978

Acknowledgements

8-fold.989 / .990
.977 / .979

We thank Dr. Lemao Liu for his help in the RNN
experiments. We thank Mr. Kaing Hour for his help
in checking the Khmer data.

Table 1: Results on Khmer data.
TOKLAT

2-fold.946 / .944
.912 / .907

4-fold.948 / .947
.913 / .911

8-fold.947 / .947
.913 / .910

References

Table 2: Results on Burmese data.
data, which is an acceptable results. While RNN approach cannot performed well on our Burmese data,
where LAT is no more than .718. We find the RNN
actually generates “Burmese-styled” Latin transcriptions but inaccurate. We thus consider the performance to be reasonable and attribute the causes forthe result to (1) the data size, which is still insufficient to support an RNN model (less than one thirdof the Khmer data), and (2) the mapping betweenBurmese and Latin scripts is complex, where manyto-many alignment between characters is common
and is difficult to model without any heuristics.
Similar to RNN experiments, CRF and SVM experiments are also cross-validated, where the eight-,four-, and two-fold results are tested for comparison. The results by CRF and SVM on Khmer andBurmese data are listed in Tables 1 and 2, respectively. The performance of the two approaches are
similar: TOK is around .99 and LAT is around .98 onKhmer data; TOK is around .95 and LAT is around.91 on Burmese data. The performances outperform
those of RNN on both data sets, respectively. We
conclude that the romanization task does not require features in a long distance, which RNN canmodel well, but precise local alignment provides useful and efficient information contributing to the performance. Therefore, we consider that it is the highquality of our annotated data, rather than a sophisticated model, that contributes more to the task.
As to engineering issues in practice, it takes hours
to train an RNN model on several thousand instancesin eight-fold cross-validation, while to train the SVM
model in KyTea only takes seconds. Therefore, weconsider RNN to be a superfluous approach for theKhmer romanization task.

6Conclusion and Future Work

We focused on the Romanization tasks on Khmerand Burmese, from the data preparation to experiments and discussion of statistical approaches. The
data prepared in this study have been released under a CC-BY-NC-SA license to promote the researchof low-resourced language processing.

[1] Rafael E. Banchs, Min Zhang, Xiangyu Duan,
Haizhou Li, and A. Kumaran. Report of NEWS2015 machine transliteration shared task. InProc. of NEWS, pages 10–23, 2015.
[2] Marta R. Costa-jussà. Moses-based official baseline for NEWS 2016. In Proc. of NEWS, pages
88–90, 2016.
[3] Chenchen Ding, Ye Kyaw Thu, Masao Utiyama,and Eiichiro Sumita. Word segmentation for
Burmese (Myanmar). ACM Transactions on
Asian and Low-Resource Language Information
Processing, 15(4):22, 2016.
[4] Andrew Finch, Lemao Liu, Xiaolin Wang, andEiichiro Sumita. Neural network transduction
models in transliteration generation. In Proc.
of NEWS, pages 61–66, 2015.
[5] Andrew Finch, Lemao Liu, Xiaolin Wang, andEiichiro Sumita. Target-bidirectional neuralmodels for machine transliteration. In Proc. ofNEWS, pages 78–82, 2016.
[6] Anoop Kunchukuttan and Pushpak Bhattacharyya. Data representation methods anduse of mined corpora for Indian languagetransliteration. In Proc. of NEWS, pages 78–82, 2015.
[7] John Lafferty, Andrew McCallum, and Fernando Pereira.
Conditional random fields:
Probabilistic models for segmenting and labeling sequence data. In Proc. of ICML, pages 282–289, 2001.
[8] Lemao Liu, Andrew Finch, Masao Utiyama,and Eiichiro Sumita. Agreement on targetbidirectional LSTMs for sequence-to-sequencelearning. In Proc. of AAAI, pages 2630–2637,2016.
[9] Graham Neubig, Yosuke Nakata, and Shinsuke
Mori. Pointwise prediction for robust, adaptable Japanese morphological analysis. In Proc.
of ACL-HLT, pages 529–533, 2011.
[10] Kishore Papineni, Salim Roukos, Todd Ward,and Wei-Jing Zhu. Bleu: A method for automatic evaluation of machine translation. InProc. of ACL, pages 311–318, 2002.
[11] Fei Sha and Fernando Pereira. Shallow parsingwith conditional random fields. In Proc. of HLTNAACT, pages 134–141, 2003.

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