Statistical Khmer Name Romanization
Chenchen Ding1(B) , Vichet Chea2 , Masao Utiyama1 , Eiichiro Sumita1 ,Sethserey Sam2 , and Sopheap Seng2
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
2Research and Development Center, National Institute of Posts,
Telecommunication and ICT, #41 Russian Federation Blvd., Phnom Penh, Cambodia{vichet.chea,sethserey.sam,sopheap.seng}@niptict.edu.kh

Abstract. We discuss and solve the task of Khmer name Romanization. Although several standard Romanization systems exist for Khmer,
conventional transcription methods are applied prevalently in practice.
These are inconsistent and complicated in some cases, due to unstable phonemic, orthographic, and etymological principles. Consequently,
statistical approaches are required for the task. We collect and manuallyalign 7, 658 Khmer name Romanization instances. The alignment scheme
is designed to reach a precise, consistent, and monotonic correspondence
between the two diﬀerent writing systems on grapheme level, throughwhich various machine learning approaches are facilitated. Experimental results demonstrate that standard approaches of conditional random ﬁelds and support vector machine supervised by the manual alignment achieve a precision of .99 on grapheme level, which outperforms astate-of-the-art recurrent neural network approach in a pure sequence-tosequence manner. The manually aligned data have been released undera license of CC BY-NC-SA for the research community.

1

Introduction

Romanization is a linguistic task used to transform a non-Latin writing system
into Latin script—which is not a huge but an important and language-speciﬁc
task in natural language processing (NLP), especially in statistical machinetranslation (SMT)—for those languages that do not use Latin script in orthography. In academia, orthographic transliteration, phonemic transcription, or mixed
systems with certain trade-oﬀ have been developed for diﬀerent languages invarious studies. In daily life, however, casual and conventional ways are morecommonly used in many languages, with varying inconsistency. Chinese (Mandarin) is a typical example of a language with a stable Romanization system,
pinyin, which is used oﬃcially and in daily life. As to the case of Japanese,
the Hepburn Romanization is the most common system for daily use, including
passports, although certain variants in notation (e.g., around long vowels) arec Springer Nature Singapore Pte Ltd. 2018

K. Hasida and W. P. Pa (Eds.): PACLING 2017, CCIS 781, pp. 179–190, 2018.
https://doi.org/10.1007/978-981-10-8438-6_15

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allowed. Korean is a case with considerable variants in Romanization, whereconventional or personalized spellings are prevalent.
In this study, we focus on the name Romanization of Khmer, a Southeast
Asian language with limited NLP studies. Similar to the languages mentioned
above, several Romanization systems have been designed for Khmer. However,the case of Khmer is most similar to that of Korean, where Romanization withconventional spellings is strongly preferable in practice. Compared with Korean,
Khmer has a more complicated phonology, less clear syllable structures, andmore etymologically oriented spellings, all of which make the task more problematic. Hence, statistical approaches based on real data are required to solve theproblem. In this paper, we provide satisfactory solutions for the Khmer nameRomanization task with detailed descriptions on data preparation, statistical
approaches, and discussions. We believe this is the ﬁrst comprehensive work forthe speciﬁc task and we have released the data prepared and used in this study
under a license of CC BY-NC-SA.1
Speciﬁcally, we collect 7, 658 Romanization instances from real Khmer personnames. We then design an alignment scheme to manually annotate the Romanization instances. The scheme is carefully designed to realize a (1) consistent, (2)precise, and (3) monotonic alignment on grapheme level. In practice, the alignment is conducted manually at the very beginning, and automatic cross-checking
with manual modiﬁcation is conducted repeatedly to clean up mistakes. Finally,
diﬃcult and unusual instances are picked up and checked with further discussions between annotators. The elaborated manual alignment on a dataset witha considerable size thus provides a solid foundation for further investigation.
In the experiments, we ﬁrst test rule-based approaches, which do not require
training data but use manual rules. The performance is mediocre. Ad-hoc rules
only provide insigniﬁcant gains. For statistical approaches, two standard machinelearning methods, conditional random fields (CRF) and support vector machine(SVM), are tested. Because of the well-prepared grapheme-level alignment, theRomanization task is simpliﬁed to be a sequence labeling task. The performanceis satisfactory: the precision on grapheme level reached .99 in cross-validation.
We also conduct a direct sequence-to-sequence experiment without using manualalignment but applying a state-of-the-art bidirectional long short-term memory(LSTM) based recurrent neural network (RNN) approach. The performance isgood, but does not match CRF’s and SVM’s results, which take advantage ofmanual alignment. Therefore, We consider that it is the high quality of ourmanually aligned data, rather than a sophisticated model, that contributes moreto the task.
The remainder of the paper is organized as follows: In Sect. 2, we introduce the background of Khmer and related work on Romanization in NLP. In
Sect. 3, we prove descriptions of the alignment scheme we designed and applied
to annotate the data. Section 4 reports the evaluation and discussion based on
experiments, and Sect. 5 provides a conclusion.
1

http://niptict.edu.kh/khmer-name-romanization-with-alignment-on-graphemelevel/.

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181

Background

Compared with the other Southeast Asian languages (e.g., Burmese and Thai),the phonology of Khmer has two obvious features. First, Khmer is not a tonal
language, which is quite unusual in Southeast Asia. To compensate for theabsence of tones, Khmer has a large set of vowel phonemes with a size ofup to (at least) 31. Second, Khmer has abundant types of consonant clusters. A
two-consonant cluster in a syllable’s onset is common (three at the maximum).
Phonotactic constraints are loose in Khmer, where complex consonant clusters
can appear at the beginning of a syllable. A complete list may contain up toaround 80 types of consonant combinations.
The two phonemic features of Khmer are deeply related to Khmer’s abugida
writing system. In a general abugida system, each standalone consonant lettercan form a complete syllable with a hidden inherent vowel. The inherent vowel
can be changed to other vowels or suppressed using various diacritic marks.
The special feature of Khmer script is that there are two series (or registers)of consonant letters, which have diﬀerent inherent vowels. The two series areusually mentioned as a-series (1st-series) and o-series (2nd-series). Furthermore,diacritics represent two vowels when added to corresponding consonant series.2
which leads to a very complicated vowel system. Another feature of Khmer script
is that the stacked consonants are very common. One reason is the abundantconsonant clusters in phonology; another reason is the etymological spelling of
Sanskrit (Pali) derived words. The stacked consonants are not strictly based on
the syllable structure. Additionally (and more problematically), the virama, i.e.,the diacritic used to suppress the inherent vowel, is absent in Khmer script,3
which makes the identiﬁcation of onset and coda diﬃcult.
Further introduction of Khmer’s phonology and script can be referred to in
the following resources available on-line: a sketchy one [3] and a detailed one [6].
We provide examples in Figs. 1 and 2 for illustration. In the ﬁrst example, three
consonant letters, 2 , 4 , and 6 are stacked to a block to represent a consonant
cluster and a dependent vowel diacritic 7 is attached to the block to form an
unbreakable unit in writing. However, the ﬁrst consonant 2 in this unit is thecoda of the ﬁrst syllable ( 1 2 ) and the left 4 and 6 are the onset of thesecond syllable ( 4 6 7 8 ). This is a typical instance of inconsistency between
phonemic analysis and writing systems. Furthermore, the ﬁrst and third units,
both of which are composed of a single consonant letter ( 1 and 8 , respectively), play diﬀerent roles. The ﬁrst unit stands for the onset and nucleus of theﬁrst syllable with the inherent vowel of letter 1 . The third unit is the coda of
the second syllable, where the inherent vowel of letter 8 is suppressed, without
any explicit notation in writing. The second example has a more consistent and2Some diacritics always stand for one vowel. There are also diacritics serving as“shifter” to switch the series of certain consonant letters, which aﬀect the vowel
sound of other diacritics.
3
Actually, Khmer script has this diacritic, called viriam, but uses obsoletely.

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Fig. 1. Khmer word with the meaning offox. The bold lines on the Khmer word show
the boundary of the unbreakable unit in
writing and the thin lines distinguish thecomponents. The order of Khmer letters ismarked by numbers. The missing 3 and5 are invisible stacking operators for generating the unit of 2 4 6 . The 7 is aseparated diacritic with two parts. IPA for
the whole word and each letter is shown.

Fig. 2. Khmer word with the meaning of teacher, borrowed from Sanskrit ācārya. Notation in this ﬁgure
is identical to that in Fig. 1. The
missing 6 is the invisible stacking operator for generating the unitof 5 7 . The third unbreakable
unit 5 7 is completely silent inpronouncing, but only written inorthography due to the etymology.

Fig. 3. A Khmer name composed of two words. The left side is the raw string pair andthe right side is the grapheme-aligned pair. 4 is the space; 7 and 9 are stacking
operators. The “.” stands for inserted inherent vowels on the Khmer side (annotatedby a hedge) and for a silent placeholder on the Latin side.

clearer match between writing and pronouncing, in that the ﬁrst and second
units ( 1 2 and 3 4 , respectively) exactly correspond to the two syllables.
The problem here is the third unit ( 5 6 ), which is totally silent, but appears
in writing only for the etymological reason. These examples illustrate the diﬃculties in even a pure phonemic transcription for Khmer script, where orthographic
and etymological facts are involved. As for Romanization in practice, the task
further suﬀers variants in mapping from phoneme to grapheme, as noted by the
Latin alphabet.
In engineering practice, the Romanization task is a string-to-string transformation, which can be cast as a simpliﬁed translation task working on grapheme
level rather than on word (or phrase) level with no (or few) reordering operations. Hence, general SMT techniques can be facilitated once training data are
prepared. The phrase-based SMT plays a role of baseline in recent workshops[1,2], whereas neural network techniques provide further gains in performance[4,5]. Although neural network-based, pure string-to-string approaches prove
powerful on diﬀerent transliteration tasks, there is still room for improvement,

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especially on tasks between diﬀerent writing systems. For example, the Thaito-English task, which is similar to our task, has relatively poor performancein NEWS 2015.4 The problem around diacritics in abugida is also stated in [7].
General techniques may oﬀer acceptable solutions overall, but specialized investigation and processing are required for further improvement on tasks for speciﬁclanguages, or language pairs.

3

Data

3.1

Collection

We collect 7, 658 real Khmer names with corresponding Romanization in pairs.
Khmer names usually consist of two words, a family name coming ﬁrst, followed
by a given name, separated by a space in writing. However, the family name is
not an obliged element, i.e., a person may have no family name but only a given
name. On the other hand, a given name may contain more than one word. As aresult, for a name containing two words, it is not always clear that it is a familygiven name pair or a two-word given name. We do not explicitly distinguish thefamily name and given name in our data. The spaces in names are treated as an
ordinary character and kept constant in Romanization. That is, Romanization isalways word-by-word, without any merging or splitting operations. An example
of a two-word Khmer name with its Romanization is illustrated on the left sideof Fig. 3.
3.2

Overall Principles in Alignment

As mentioned, phonemic syllables and unbreakable writing units do not match
well in Khmer script. Consistent alignment principles are thus diﬃcult to establish if the syllables or units are taken as atoms in processing. Furthermore,
there are numerous types of syllables and writing units that may contain up to
four phonemes, which are too complex for statistical model learning becauseof sparseness. Based on these facts, we prefer a pure character-level alignment,
where standalone consonant letters, diacritics, and the invisible staking operator
are separated and treated equally as grapheme on the Khmer side.
A consequent problem is the inherent vowels, once we completely ignore
the syllable structure in a character-based alignment. 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) toestablish a consistent alignment. This insertion is thus decisive based on thesurface spelling, and the ambiguity on the presence or absence of the inherent
4

The original names are western names in the Thai-to-English task, which may makethe grapheme correspondence more varied and inconsistent.

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vowel is converted to whether the inserted mark is silent.5 As a result, theproblem is treated as uniformly as the other silent characters.
The right side of Fig. 3 illustrates an alignment example. The consonant letters here are 1 , 3 , 5 , 6 , 8 , and 10 , where 3 and 5 are bare consonant
letters,6 after which the inherent vowel is inserted (noted by a dot here andindicated by a wedge without original index). Then a character-by-characteralignment can be established.7 According to the overall principles, Khmer consonant letters are aligned to Latin consonant graphemes; diacritics, including
inserted inherent vowels, are aligned to Latin vowel graphemes; and any silent
part is aligned to a placeholder.8 Further alignment details caused by speciﬁc
use is described in the following subsection.
In Fig. 3 as well as in our practice, we use the same mark for the Khmer side
inserted inherent vowel and the silent placeholder on the Romanization side. This
introduces no confusion because the mark is decisively inserted on the Khmer
side and decisively deleted in the Romanization results.
3.3

Special Cases in Alignment

The alignment under our overall principles is almost monotonic and one-to-one.
However, there are two speciﬁc cases to be discussed: (1) multiple diacritics for
one consonant letter and (2) stacked consonants letters for a single phoneme.
The ﬁrst case is mainly caused by the anusvara (adding nasal ending), visarga(adding aspiration ending), and series shifters changing the a-/o-series of consonant letters. Figure 4 illustrates the examples. When a consonant letter takes ananusvara directly, it is not bare anymore, so the Latin letters for inherent vowel
and the nasal ending (commonly transcribed into M) are taken as one graphemealigned to the anusvara. The anusvara can, however, modify other diacritics to
add a nasal ending, where only the M endings are aligned to anusvara and thevowel value is taken by the preceding diacritics. The ﬁrst two instances in Fig. 4
show the diﬀerence in alignment on anusvara. The alignment around visarga,
which is commonly transcribed into S, is identical to that around anusvara. The
middle two instances in Fig. 4 show the alignment around the series shifter, which
is similar to the anusvara (visarga), but inserted between a consonant letter andother diacritics. The shifter is actually modifying the preceding consonant letter,so the value of the vowel will be aﬀorded by other diacritics if there is any. The
shifter itself has no speciﬁc value unless it is the only diacritic of a consonant.
5

Generally, the diacritics in an abugida system are observed as vowel shifters to change
the inherent vowel. From the viewpoint of this study, the diacritics are actuallytreated as vowel notes and the inherent vowel is treated as one with a zero-alternant
form, which the insertion processing makes explicit.
6
2 and 11 are diacritics for 1 and 10 , respectively, and 6 and 8 are followed
by staking operators. So these four consonant letters are not bare.
7
As the task is to transform Khmer script to Latin script, the graphemes are notguaranteed to be single letters on the Romanization side, e.g., the 5 corresponds
to CH here.
8
The stacking operator is always aligned to a silent placeholder.

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Fig. 4. Alignment principles around anusvara (visarga), series shifters, and an alignment example for a Chinese-style name with both diacritics.

Fig. 5. Alignment examples of two western-style names, where the consonants Z and
F are non-native phonemes of Khmer. The original value (common transcription) ofconsonant letters combined to represent the two sounds is placed upward.

The rightmost example in Fig. 4 is a real name taking both anusvara and theshifter simultaneously, aligned according to our principles.
The second case is caused by certain non-native consonants in Khmer, mainly
appearing in loanwords, and here, appearing in westernized names. Figure 5 provides two real examples. It can be observed that Z is represented by stacked
Khmer letters for H and S, and F is represented by stacked Khmer letters for H
and V. The consonant letter of H is commonly stacked over another consonant
letter of an approximate pronunciation to represent a non-native consonant.
Therefore, the corresponding Latin consonant graphemes are aligned to the second Khmer letters and the ﬁrst Khmer letter H is aligned to a silent placeholder.
In Fig. 5 (on the right), the stacked consonants for F are further modiﬁed by two
diacritics, where the ﬁrst is a series shifter.
As illustrated in this subsection, the combination of consonant letters andvarious diacritics is complicated in Khmer script, and the grapheme-level alignment we designed can cover diﬀerent cases consistently, to oﬀer a monotonic
and one-to-one alignment. However, the Romanization of Khmer characters is
not totally position-free under our alignment scheme, and at least a window oftri-gram on Khmer characters is required to provide enough information for acorrect Romanization.

4

Experiment

4.1

Questions and Approaches

The well-aligned data prepared in this study have already provided a solid foundation of the Romanization task. Therefore, we investigate the following two

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questions through experiments: (1) to what extent can statistical approaches
help the task and (2) to what extent can our manual alignment help statistical
approaches.
For the ﬁrst question, we ﬁrst try a rule-based transcription to investigate theperformance. The rules used are edited manually, based on conventional spellingsin Romanization rather than academic standards. Then we try a state-of-the-art
neural network-based sequence-to-sequence approach trained on surface string
pairs in our data, without using the alignment. The neural network outperforms
the rule-based approach by a large margin. The results illustrate the necessityof real data and statistical approaches in this task.
For the second question, we further try two standard and widely used machinelearning approaches, CRF and SVM, trained on the grapheme-level alignment.
Because the alignment has cleaned up two diﬃcult problems in transformation,i.e., source-side segmentation (actually, character-by-character) and source-totarget alignment, the task is converted to a sequence labeling task, to whichthese compact and eﬃcient approaches are applicable. The results yielded by thetwo approaches outperform the neural network’s results, demonstrating that the
manual alignment does provide useful information and boosts the performanceeﬃciently.
The details of the experiments are described in the following subsections.
Experimental results are evaluated in two ways: the accuracy on source-to-target
grapheme transcription (GRAPH) and the accuracy on target strings (LATIN).
GRAPH is based on grapheme-level alignment, where all Khmer letters, including the inserted inherent vowel, are counted in the calculation.9 GRAPH cannot
be applied to the sequence-to-sequence experiments as the alignment is not an
explicit variable in the processing and ﬁnal results. For LATIN, we simply apply
the BLEU score [11], the most common measure in SMT, on alphabet level,
where silent placeholders in Romanization are deleted before calculation.10
4.2

Rule-Based Transcription

Although the Romanization of Khmer is not consistent, there is preference foreach letter. Figures 6 and 7 show the most preferred transcription of Khmer
letters. A naı̈ve mapping based on the listed transcriptions cannot reach a satisfactory performance. The accuracy is .746 on GRAPH and .515 on LATIN. When
we focus more on some conventional spelling features, so as to double the consonant letters after short vowels or to transform speciﬁc stacking consonants,(e.g., the case in Fig. 5), we have a better result, a GRAPH of .909 and a LATIN of.821. Further improvement is diﬃcult. We try ad-hoc rules in an exhaustive way
and ﬁnd the upper boundary of the rule-based mapping to be around .95 for

9Spaces are not counted because they are always maintained constant. Stacking operators as well as other silent Khmer letters are counted.
10Spaces are taken as one character in the BLEU calculation.

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GRAPH and .88 for LATIN. Although the performance is not perfect, we consider
it reasonable and acceptable in terms of simplicity.11
4.3

Sequence-to-Sequence Transliteration

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 LSTM-based RNN approach with a bidirectional searchin decoding12 [9]. The approach performs well on diﬀerent transliteration tasks.
The experiment is conducted using an eight-fold cross-validation on our data
without using manual alignment. The entire data are split into eight parts, witheach part taken as a test set and the left parts used for training. As separated
development data are needed for tuning the iteration times, one-thirtieth of the
training data is sampled. Other hyper-parameters are based according to theoriginal paper: 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 .
The result of LSTM-based RNN reaches .953 in terms of LATIN. Comparedwith the rule-based mapping result, a machine learning approach clearly performs better on the task, even without applying any speciﬁc a priori knowledge.
The comparison of the two approaches provides a clear and solid answer to theﬁrst question.
4.4

Alignment-Based Sequence Labeling

We test two standard machine learning approaches, CRF and SVM, by handlingthe task in a sequence labeling manner. That is, the Latin graphemes (including
the silent placeholder) are tried as labels for each Khmer character.
We use the CRF++ toolkit13 [8,12] and the KyTea toolkit14 [10] for CRF
and SVM experiments, respectively. Both toolkits are open-sourced. Similar to
RNN experiments, CRF and SVM experiments are also cross-validated, where
the eight-, four-, and two-fold results are tested for comparison. As to the settingsof the two approaches, we basically use up to tri-gram on Khmer characters as
input features. As mentioned, this is the minimum size of a window to provide
adequate information under our alignment scheme. Speciﬁcally, the -charn andthe -charw options are set to three for KyTea. The features used for CRF++
are Cnn+k (k ∈ [1, 2], n ∈ [−k, 0]) for character sequences and Cn (n ∈ [−2, 2]) for
single characters. As the training speed of KyTea is very fast under this setting,
we ultimately try an exhaustive leave-one-out experiment, in which each instanceis left for testing and all the rest instances are used for training. Table 1 lists theevaluation on the whole dataset.
11

A Python implementation for the rule-based transcription with diﬀerent layers ofrules is available at http://www2.nict.go.jp/astrec-att/member/mutiyama/software.
html.
12
An open-sourced tool is available at https://github.com/lemaoliu/Agtarbidir.
13
http://taku910.github.io/crfpp/.
14
http://www.phontron.com/kytea/.

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Fig. 6. Preferable transcription for Khmer consonant letters. A-series and o-series letters are listed. The gray letters are rarely used and never appear in our data. The
ﬁnal diacritics in the two series are the two series shifters, which change the consonant
letters in the other series to their own series.

Fig. 7. Preferable transcription for diﬀerent Khmer vowel diacritics and letters. Diacritics with consonant series-dependent transcriptions are listed on the upper left, aseries, and o-series respectively. Diacritics with identical transcriptions are listed at thebottom. These diacritics may stand for diﬀerent vowels for consonants from diﬀerent
series, without being reﬂected in Romanization. In the upper right, standalone lettersfor vowels are listed. Rare vowels not appearing in our data are marked in gray.

Table 1. Evaluation results (GRAPH/LATIN) of CRF and SVM in cross-validation.
2-fold4-fold8-foldleave-1-out

CRF .987/.974 .988/.976 .989/.977 —SVM .988/.977 .989/.978 .990/.979 .990/.980

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The performance of the two approaches is nearly identical. The performanceon GRAPH is around .99 and on LATIN is around .98, which outperforms the RNN’s.95. The results is satisfactory and reasonable. As to the answer to the second
question, we conclude that the Khmer Romanization task does not require features in a long distance, which RNN can model well, but precise local alignment
provides useful and eﬃcient information contributing to the performance.
As to engineering issues in practice, it takes hours to train an RNN model
on more than 6, 000 instances in eight-fold cross-validation, while to train the
SVM model in KyTea only takes seconds. Therefore, we consider RNN to be asuperﬂuous approach for the Khmer Romanization task. As Table 1 shows, two-,four-, eight-fold, and leave-one-out results actually do not diﬀer much, whichindicates that several thousand instances are suﬃcient for the model training.

5

Conclusion

In this paper, we focus on the task of Khmer name Romanization, a task nothuge, but with its own diﬃculties. By collecting and elaborately aligning morethan 7, 000 real instances, we provide a solid foundation for the task. Experimental results demonstrate that a rule-based approach is not suﬃcient to solve
the Romanization task, while statistical machine learning approaches trained on
our manual alignment can achieve an accuracy of .99 on the grapheme level.
Therefore, we believe that the Khmer name Romanization task has actually
been solved, provided our data and standard machine learning techniques. As
our manually aligned dataset plays a key role in solving the task, we release it
under a license of CC BY-NC-SA for the research community.

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