言語処理学会 第21回年次大会 発表論文集 (2015年3月)

Rule Based Katakana to Myanmar Transliteration
for Post-editing Machine Translation
Hay Mar Soe Naing† , Ye Kyaw Thu‡ , Win Pa Pa† , Hiroaki Kato§ , Andrew Finch‡ ,Eiichiro Sumita‡ , Chiori Hori§† Natrual Language Processing Lab., UCSY, Yangon{haymarsoenaing,winpapa}@ucsy.edu.mm‡ Multilingual Translation Lab., NICT, Japan{yekyawthu, andrew.finch, eiichiro.sumita}@nict.go.jp§ Spoken Language Communication Lab., NICT, Japan{kato.hiroaki, chiori.hori}@nict.go.jp
1Introduction

Phrase based statistical machine translation (PBSMT) is a current state-of-the-art approach to machine translation, however its
outputs often contain various types of errors such as lexical errors, and syntax errors(Koehn et al., 2003)(Bojar et al., 2013)(Bojar,2011b). Incorporating deep linguistic knowledge directly into PBSMT is not easy andrarely leads to improvements in translation
performance (Bojar, 2011a). One of the possible solution is to make automatic corrections
on translated output in a post-editing process.
This paper presents a rule based post-editing
scheme for ﬁxing translation errors based onout of vocabulary (OOV) Katakana words produced by Japanese to Myanmar PBSMT. Ourexperiments indicate that applying rule based
Katakana to Myanmar transliteration leads tosubstantial improvements of translation quality both in terms of BLEU scores and OOVcoverage.

2

Releated Work

Generally there are two main classes of approach for automatic post-editing, one contains the statistical approaches and anothercontains the rule based approaches. The
ﬁrst reported results of statistical based postediting was proposed by (Simard et al., 2007).
They used a PBSMT system trained on monolingual outputs from a rule-based system as
the source and the human-checked referencetranslations as the target to achieve a large
improvement in translation quailty. They also
compared the performance of the post-edited
rule-based system to a baseline PBSMT system and reported that their system outperformed the baseline. Another statistical post-editing approach was proposed by (Oﬂazer andEl-Kahlout, 2007) on English to Turkish.
In (Finch et al., 2011) a statistical method
was proposed for transliterating OOVs produced on the target side by a PBSMT system.
They evaluated their method on Japaneseto-English translation using a human evaluation which showed that transliterating OOVs
can improve the quality of machine translation output. Our approach is closely related to theirs, but uses a rule-based strategy for transliteration, since there is almost
no data available to train a statistical system
for Japanese-to-Myanmar transliteration.

3 Japanese to Myanmar
Transliteration Mapping
We deﬁne a transliteration mapping tablefrom Japanese letters to Myanmar (Burmese)letters. This proposed mapping table on theJapanese side comprises of 61 monographs anddigraphs of Katakana syllabograms, 40 monographs and digraphs with diacritics, 98 extended characters and 128 combinations of ﬁnal nasal monographs 1 . Each Japanese syllable is transliterated into a corresponding
Myanmar letter based on the mapping table.
However, we cannot express the best approximate orthoepy for a given word with only thistransliteration mapping. Therefore we augment this table with a set of rules to make arule-based transliteration system that can produce the closest possible pronunciation under
the constraints imposed by the Myanmar phonetics. Some mapping examples are shown inTable 1.

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http://en.wikipedia.org/wiki/Katakana

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Figure 1: Overall Workﬂow of Post-editing Process
Jp − Syllables
あ ア
い イ
う ウ
え エ
お オ
か カ
き キく ク
け ケ
こ コ
しゃ シャ
しゅ シュ
しょ ショ
あん アン
いん イン

M y − Syllables

is actually pronounced as the lengthened ‘o’sound.
အိ [ʔḭ]Precondition 3: Long ‘ee’sound apအ [ʔṵ]
pending with ‘i’vowel: When the symအဲ [ʔɛ́]bol い (i) is appended to a syllable ending inအိ [ʔò]
え (e) within the same morpheme, the ‘ei’
ခ [kʰa̰]isactually pronounced as the lengthened ‘e’ခိ [kʰḭ]sound with a small number of exceptions.
ခ [kʰṵ]We show the Japanese to Myanmar transliterခဲ [kʰɛ́]ation of long vowel orthoepy in Table 2.
ခိ [kʰò]Rule 2: Consonant Doubling: Theယှ [ʃa̰]small tsu in Hiragana and Katakana areယ [ʃṵ]ေယှာ [ʃɔ́]mainly used to indicate the geminate consoအန်း [ʔáɴ]nant means that the next character is doubledအင်း [ʔɪ ́ɴ]and is also known as ‘sokuon’in Japanese.
It has two realizations. More precisely, when
Table 1: Example of Transliteration Mapping
followed by a stop or aﬀricate consonant
like “p, t, k, ch, or ts”, the closure partof the following consonant is lengthened by
4 Rule-Based Transliteration
one mora. The sokuon represents this silent
Systemclosure. In this case, the hyphen symbol
In general, all “ん”, “っ”, and “ー” ‘-’ is used to render a short break in thedo not have unique sounds but indicate retencontinuous pronunciation of a given word.
tion of a sound for a deﬁnite period of a time.
For example:
Therefore, they are called as moraic phonemes
kippu=>ခိ -ပ /kʰḭ-pṵ/or special morae.
kitte=>ခိ -တဲ /kʰḭ-tɛ́/Rule 1: Long Vowels:
gakkoo=>ဂ-ခိ း /ga̰-kʰó/matchi=>မ-ချိ /ma̰-tɕʰḭ/There are three types of long vowel inJapanese, and these form the three precondiIf the small tsu is followed by a frications to this rule. All are transliterated using
tive consonant like “s, sh, h or f” , thethe Myanmar symbol ‘း’ (wit-sa-bauk) canfollowing consonant is lengthened by one
be produce a longer high tone but it must folmora. We use the following set of characters
low another character which it modiﬁes, simito represent this class of doubled consonant:lar to the ‘ー’character in Japanese.
(စ်), (ှ်), (ဟ်) and (ဖ်). For instance, the English
Precondition 1: Long Vowel and Doucharacter ‘F’is transliterated as ‘အက်(ဖ်)’
ble Vowels: Long vowels are diﬀerently tran, as in: Kissaten Kafe=>ခိ (စ်)စတန် /kʰḭ(s)sa̰ tàɴ/scribed Katakana and Hiragana. In Katakana,a simple dash ‘ー’character is used to exAs an aside, the subset (စ်),(ခ်),(ဖ်) . are used
tend the vowel’s length. In Hiragana, thefor foreign names as they can approximate
same vowel with the preceeding mora is douthe native pronunciation.
bled: ‘aa’, ‘ii’, ‘uu’, ‘ee’, ‘oo’.
Precondition 2: Long ‘oo’sound appending with ‘u’vowel: When the symRule 3: The Moraic Nasal: The moraic
bol う (u) is appended to a syllable ending
nasal ん or ン has many realizations based onin お (o) within the same morpheme, ‘ou’ the position it appears in a word. Speciﬁcally,Romanization
aiueokakiku
kekoshashu
shoaninအ [ʔɑ̰]― 258 ―

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All Rights Reserved.

Vowelsaiueo

Hiragana − Katakana
ああ アー
いい イー
うう ウー
ええ えい エー
おお おう オーM yanmar − Orthoepy
အား /á/အီ း /í/အ း /ú/အဲ း /ɛ́/အိ း /ó/Exampleパーティー ပါးတီ း /pá tí/
ビール ဘီ း /bí jṵ/
スーパー စ းပါး /sú pá/チョコㇾート ေချာခိ ရဲ းတိ /tɕʰɔ́ kʰò jɛ́ tò/
コーヒー ခိ းဟီ း /kʰó hí/Table 2: Long Vowel Orthoepyit is bilabial [m] before consonants like “b,p, or m” . It is velar [ŋ] before “k or g”
and is alveolar [n] before “t, d, ch, j, ts, n, r,z”consonants. Moreover if it is followed by avowel, semivowel, and fricative or it is at the endof the utterance, it can be nasalized as uvular[N].
The Myanmar script contains 33 character toindicate the initial consonant of a syllable. Thecharacter ‘င’[ŋ], ‘ည’[ɲ], ‘န’[n], ‘ဏ’[n], and ‘မ’ [m] are nasal consonants in theMyanmar phonology 2 . In our approach, we use
the special characters (မ်), (င်) and (န်) to express
the native pronunciation as closely as possiblefor different moraic nasal. The symbol (မ်) isused to show the articulation of both lips, (င်) is
designated for pronouncing with the back of thetongue and touching or near the soft plate and thecharacter (န်) is applied to articulate with the tipof the tongue near the gum ridge.
For example:
shinbun => ှင်း(မ်)ဘွ န်း /ʃɪ ́ɴ(m)bʊ́ɴ/sentaa=> စဲ န်း(န်)တား /sáɴ(n)tá/
sankyuu => စန်း(င်)ခယ း /sáɴ(ŋ) kʰú/
The Japanese language has adopted many loan
words from foreign languages. As a consequence,
it is hard to express the closest pronunciation offoreign words based on the basic Katakana characters. Therefore, a new extended Katakana set
was developed by adding small vowels “a, i, u,e, o”for several loan words. Some of the extended Katakana forms are listed in Table 3. The
frequently used extended Katakana characters arealso included in our transliteration mapping.

5Experimental Setup

We used the multilingual Basic Travel Expressions Corpus (BTEC), which is a collection of
travel-related expressions (Kikui et al., 2003). In
this experiment, we used 155,069 sentences fortraining the machine translation models and 16142
http://en.wikipedia.org/wiki/Burmese_alphabet

test sentences. The test sentences were translated by passing the OOVs through into the output. Of these 1614 test sentences translations 134
contained untranslated Katakana words. We randomly sampled 100 sentences from this set of 134
translations for evaluation.
For PBSMT we used MOSES (Koehn et al.,2007). A 5-gram target language model wasbuilt using the SRILM toolkit 3 . The decoding
was done using MOSES. The overall workflow of
post-editing is as shown in Figure 1.
We transliterated OOV Katakana words withour rule based Katakana to Myanmar converter,and replaced the Katakana with the resulting
transliterations in the output. For the evaluation
we used BLEU (Papineni et al., 2002) and OOVrate as a percentage.

6 Results and Discussion
The baseline was a standard phrase-based
SMT system without tuning. Table 4 shows
the performance on the post-edited target text
measured by BLEU and OOV percentages. Our
results show that post-editing by rule based
Katakana to Myanmar translation outperformed
baseline result in terms of both BLEU score(+19.39) and OOV percentage (-9.33). Visualinspection of the types of OOVs the system
was able to transliterate showed that most of
the Katakana unknown words of BTEC corpus
were named entities: that is names of people,places, companies etc., and our impression wasthat rule based post-editing in most cases madethese understandable to a Myanmar speaker. Thefollowing is an example of post-editing on apersonal name written in Katakana.
Original translated output:ကန် ေတာ် က ヨシノシゲコ ပါ ။
After post-editing:ကန် ေတာ် က ယိ ှိ ိ ှိ ဂဲ ခိ ပါ ။[tɕʊ̀ɴ tɔ̀ ka̰ jò ʃḭ nò ʃḭ gɛ́ kʰò pà]
3― 259 ―http://www.speech.sri.com/projects/srilm/

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Consonants
fv
tdshchja
ファဖှ
ヴァဗ
-i
フィဖှိ
ヴィဗိ
ティတိ
ディဒိ
-u
トゥတ
ドゥဒ
-e
フェဖှဲ
ヴェဗဲ
シェှဲ
チェချဲ
ジェဂျဲ

oフォဖိ
ヴォဗိ
-Table 3: The Extended Katakana CharactersMethodBaseline
P ost − Editing

BLEU14.7034.09

BLEU (no OOV)
14.90
34.11

OOV Ondřej Bojar. 2011a. Rich Morphology and What Can
10.22 We Expect from Hybrid Approaches to MT. In0.89 vited talk at International Workshop on Using Linguistic Information for Hybrid Machine Translation(LIHMT-2011), November.

Table 4: Performance on Machine Translation
Measured by BLEU score and OOV rate. In
the BLEU (no OOV) evaluation, all remaining
untranslated Japanese was deleted from theoutput.
However, the transliteration of some Katakana
OOV words can sometimes lead to meaningless
output, for example when the word is not atransliteration:ဇက် ေကာ コリ ေန ပါ တယ် ။[zɛʔ tɕɔ́ コリ nè pà tɛ̀](I have a stiff neck)Ondřej Bojar.
2011b.
Analyzing Error Types
in English-Czech Machine Translation. Prague
Bulletin of Mathematical Linguistics, 95:63--76,March.
Andrew Finch, Keiji Yasuda, Hideo Okuma, Eiichiro
Sumita, and Satoshi Nakamura. 2011. A bayesianmodel of transliteration and its human evaluation
when integrated into a machine translation system.
IEICE TRANSACTIONS on Information and Systems, E94-D(10):1889--1900, 10.
G. Kikui, E. Sumita, T. Takezawa, and S. Yamamoto.
2003. Creating corpora for speech-to-speech translation. In Proc. of EUROSPEECH-03, pages 381-384.
Philipp Koehn, Franz Josef Och, and Daniel Marcu.
2003. Statistical Phrase-Based Translation. In Proc.
HLT/NAACL 2003, pages 48--54.

トゲ လက် ကိ စ း သွ ား တယ် ။[トゲ lɛʔ kò sú θwá tɛ̀](My hand was pricked by a thorn)

7 Conclusion
The main contribution of this paper is to propose a rule-based Katakana to Myanmar transliteration technique for SMT post-editing. From theresults, we can conclude that using this approach
can handle untranslated Katakana OOV errors effectively, increasing translation performance andreducing the number of OOVs in the translations.

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