Empirical Dependency-Based Head Finalization for Statistical
Chinese-, English-, and French-to-Myanmar (Burmese) Machine Translation
Chenchen Ding† , Ye Kyaw Thu‡ , Masao Utiyama‡ , Andrew Finch‡ , Eiichiro Sumita‡
Department of Computer Science, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan‡
Multilingual Translation Laboratory,
National Institute of Information and Communications Technology
3-5 Hikaridai, Seikacho, Sorakugun, Kyoto, 619-0289, Japan††‡

tei@mibel.cs.tsukuba.ac.jp{yekyawthu, mutiyama, andrew.finch, eiichiro.sumita}@nict.go.jp

Abstract
We conduct dependency-based head finalization for statistical machine translation (SMT) for Myanmar (Burmese). Although Myanmar is an understudied language, linguistically
it is a head-final language with similar syntax to Japanese andKorean. So, applying the efficient techniques of Japaneseand Korean processing to Myanmar is a natural idea. Our
approach is a combination of two approaches. The first isa head-driven phrase structure grammar (HPSG) based head
finalization for English-to-Japanese translation, the second
is dependency-based pre-ordering originally designed forEnglish-to-Korean translation. We experiment on Chinese-,
English-, and French-to-Myanmar translation, using a statistical pre-ordering approach as a comparison method. Experimental results show the dependency-based head finalization was able to consistently improve a baseline SMT system, for different source languages and different segmentation schemes for the Myanmar language.

1. Introduction
The state-of-the-art techniques of statistical machine translation (SMT) [1, 2] demonstrate good performance on translation of languages with relatively similar word orders [3].
However, word reordering is a problematic issue for language pairs with significantly different word orders, suchas the translation between a subject-verb-object (SVO) language and a subject-object-verb (SOV) language [4].
To resolve the word reordering problem in SMT, a lineof research handles the word reordering as a separate preprocess, which is referred as pre-ordering. In pre-ordering,
the word order on source-side is arranged into the targetside word order, before a standard SMT system is applied,on both training and decoding phases. The pre-ordering process can be realized in either a rule-based way or a statisticalway. Generally, a rule-based approach needs a high-precisionparser and effective manually designed rules; and a statistical
approach needs data for model training.

An effective rule-based approach, head finalization has
been proposed for English-to-Japanese translation [4]. The
approach takes advantage of the head final property ofJapanese on the target-side. It designs a head finalization rule
to move the head word based on the parsing result by a headdriven phrase structure grammar (HPSG) parser. Generally,
the idea can be applied to other SVO-to-Japanese translation
tasks, such as its application in Chinese-to-Japanese translation [5]. However, an HPSG parser is not available for many
languages, which prevents the HPSG-based head finalizationfrom being applied to more languages. On the other hand,dependency parsers are available for more languages. A typical rule-based pre-ordering using dependency structure wasproposed in [6]. Their approach used a rule set to arrange theorder of a head word together with its modifiers.
In this paper, we explore dependency-based head finalization for an understudied language, Myanmar1 . We use thedependency structure to realize the head finalization of [4].
Because the head finalization only moves a head word after
all its modifiers, the proposed dependency-based head finalization is a simplified version of [6], which keeps the order ofmodifiers unchanged. So, our approach is simple and widely
applicable for different source languages. On the targetside, there are no standard part-of-speech set and morpheme
analysis tools available for Myanmar word segmentation yet,so we employ two word segmentation schemes: syllablebased and dictionary-based maximum matching. Experiments on Chinese-, English-, and French-to-Myanmar translation show that simple head finalization can efficiently andstably improve a baseline SMT system, no matter what thesource-side language is or which segmentation scheme isused. We use a statistical pre-ordering approach [7] as acomparison method. We observe it performs well on certain
situations, but it is sensitive to the source-side language andsegmentation schemes.
1 The language may be more referred as Burmese in English though, inthis paper, we refer it consistently as Myanmar.

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Myanmarသူူ သည်စာအု ပ်

ကို

ဆရာ

အား

တပး

သည်

give

present
markerEnglish literallyhenominative
markerbook

accusative
markerteacher

dativemarkerJapanese

彼

が

本

を

先生

に

あげる

Korean그가책을선생님 에게

올린다်ကို ဆရာအား တပးသညူ
် ” (English translation “he gives the book toူ သည် စာအု ပူ
Figure 1: Example of a Myanmar sentence “ သူthe teacher”). The first row shows the morphemes in the Myanmar sentence, one-box-one-morpheme. Content morphemes areillustrated in black and functional morphemes are in gray. The second row is the English literal translations of them. In the
two lower rows, Japanese and Korean translations of the Myanmar sentence are also shown, morpheme-by-morpheme. Both theJapanese and Korean sentences are grammatically correct, from which the syntactic similarity can be observed. The right-mostboxes in Japanese and Korean sentences, which contain the verbs, should be noticed. The corresponding parts of Myanmar
present marker in these two languages are inflection endings which cannot be detached from the verb stems (marked by gray, inthe case of Korean, more correctly, the “ ㄴ다 ” part). While Myanmar has a completely detachable marker from the verb stem.
This paper is organized as follows. In Section 2, we givean introduction to the Myanmar language. In Section 3, we
discuss related approaches. In Section 4, we describe theproposed approach. In Section 5, we show the experimental results and present a discussion. Section 6 contains theconclusion and future work.

2. Myanmar Language
Myanmar is an SOV language that demonstrates a consistenthead-final typology. Syntactically, Myanmar is quite similar to Japanese and Korean, where functional morphemes
succeed content morphemes, and verb phrases succeed nounphrases. We show an example in Fig. 1 to show the featuresof Myanmar and its similarity to Japanese and Korean.
On the other hand, unlike Japanese and Korean, which
are typical agglutinative languages, Myanmar is an analytic language, in which the morphemes are without inflection. This is because Myanmar is a monosyllabic language
originally, where morphemes are only composed by noninflected single syllables. Although Buddhism-related loanwords from the Pali language and modern loanwords fromwestern languages have introduced polysyllabic morphemes
into Myanmar, the basic framework of syntax has not been
affected.

3. Related WorkAs mentioned, Myanmar is an understudied language thathas quite similar (or, even simpler) characteristics toJapanese and Korean, both of which are well studied. A
natural idea is that we can transfer the Japanese or Korean
language processing techniques to Myanmar.
HPSG-based head finalization [4] and dependency-based

pre-ordering [6] are two typical rule-based pre-ordering approaches. Originally, the former was designed for Japaneseand the latter for Korean. Further differences between the
two approaches first lies in the linguistic formulation theyused, which leads to differences in their rule sets. Essentially,
there is only one rule in the HPSG-based head finalization,that is the head finalization rule itself. The simplicity of therule set can be attributed to the sophisticated analysis by an
HPSG parser, which shows the phrase structural as well as
the syntactic head. On the other hand, the rule set in [6] contains about 20 rules, in order to arrange the position of a head
word with its modifiers. It can be observed that a good HPSG
parser is required for [4] if we want to expand the approach tomore source-side languages, despite the simple rule. Whilea dependency parser is available for more languages, the rule
set in [6] is dependent on the part-of-speech (POS) tag set
and dependency arc label set of the dependency parser used.
The approach used in our experiments combines the simplicities of the two previous approaches. We use dependency
parsers to conduct the head finalization alone without touching the arrangement of various types of modifiers.
There are also statistical pre-ordering approaches. The
work of [8, 9] are early syntax-oriented approaches, that they
introduce separate reordering modules into SMT systems.
Recently, the approach in [10] learns pre-ordering automatically from an aligned corpus. This approach achieves nearlysame performance as the rule-based approach of [6] (Table
4 in [10]). In [7], a method to learn a discriminative parser
for pre-ordering from an aligned parallel training corpus is
proposed. The approach takes the derivation tree as a latent variable and trains a model to maximize reordering measures. The approach is fully unsupervised but needs highquality training data (i.e., a word-aligned parallel corpus). In

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i

’ll

i

pepperoni

have

a

slice

and

of

pizza

mushroom

with

with

pepperoni

andmushroom

of

slice

have

pizza

a

’ll

Figure 2: Pre-ordering example of English sentence “i ‘ll have a slice of pizza with pepperoni and mushroom”.

first

dial

zero

,

then

dial

the

number

you

first

zero

dial

,

then

you

call

to

would

like

would

the

like

to

call

numberdial

Figure 3: Pre-ordering example of English sentence “first dial zero , then dial the number you would like to call”.
the experiments reported in [7], they show the model trainedby a manually aligned parallel corpus outperforms the modeltrained by an automatically aligned parallel corpus of morethan ten times the size. We take the approach of [7] as a baseline in our experiment, to explore the different characteristicsof the rule-based and statistical pre-ordering approaches.

4. Head Finalization for Myanmar

noun. While we consider auxiliary verb should be the headof a verb, and actually, in typical head-final languages theauxiliary verbs are always placed after the verb. So we arrange auxiliary verbs after their head verb. E.g., in Fig. 2, we
keep the “‘ll” after the verb “have”; and in Fig. 3, “to ” after“call”.
We describe detailed source-language dependent featuresin the appendices.

4.1. Basic Principle

4.2. Myanmar Oriented Process

The dependency-based head finalization used in our experiment is according the following principle.

In the original head finalization approach, a morpheme generation process is used also to generate certain target-side
grammatical markers which are absent in the source-side language. Specifically, in [4], they insert three types of tag for
topic marker, nominative marker, and accusative marker inthe source-side English. However, this issue is not so serious
for Myanmar because it has a strong tendency to omit thesegrammatic markers as long as no ambiguity arises2 . So we
do not apply this generation process in [4] in our approach.
On the other hand, negation in Myanmar, unlike inJapanese or in Korean, where it is realized by a negation auxiliary word as a suffix of the verb, is realized by a prefix “ မ ”
before the verb3 . Further, as a collocation of the negation preူ း ” must succeed the verb. Finally,
fix, a negation suffix “ ဘူthe prefix and suffix surround a verb to form a negation. The
phenomenon is rather like the “ne ... pas” in French. However, the “pas” is not fixed and can be replace with “plus”• To move the head word after all its modifiers, but
1. do not break a coordination structure;
2. do not cross a punctuation mark;3. auxiliary verbs come after their head verb.
We show two examples of the English sentence in Fig. 2
and Fig. 3. The dependency structures are marked by arcs
over the words. In Fig. 2, “pepperoni and mushroom” is acoordination structure with the first word “pepperoni” as ahead word. We do not apply head finalization in this kind of
structure in order to keep the original order of coordinating
components. In Fig. 3, the root word of the sentence, i.e.
the first dial, does not cross the comma after it. We disable
head finalization in this situation to avoid excess reordering
between clauses.
As to the auxiliary verbs (3), many widely-used dependency parsers handle this kind of functional word as the modifier of a verb, just as an article becoming the modifier of a

2 Actually, the example of a Myanmar sentence given in Fig. 1 is a quite
formal expression which is rare in daily communication. We show it mainly
to illustrate the syntactical similarity to Japanese and Korean.
3 In Korean, there are also alternative prefixes used instead of negation
suffixes. While in Myanmar, the negation prefix is used consistently.

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or “jamais” and so forth according the meaning in French,
while the prefix and suffix are fixed in Myanmar. We use aneg tag for the negation suffix generation. Specifically, thenegation word of a verb is placed immediately before theverb and the neg tag is inserted immediately after the verb.
We use the same strategy as [4] to delete the articles inthe source-side language (if any). As shown in Fig. 2 andFig. 3, the “a” and “the” are deleted (marked in gray).

5. Experiments
5.1. Corpus and Settings
We use Basic Travel Expression Corpus (BTEC) [11] in theexperiments. The source languages are Chinese (zh), English (en), French (fr) and the target language is Myanmar(my). The corpus statistics are is shown in Tables 1, 2 and 3.
Specifically, the training, development, and test data for zh-,en-, and fr-my translations contain identical Myanmar sentences. We use two segmentation schemes for the morpheme
process of Myanmar sentences. One is syllable-based (syl)[12] and the other is maximum marching (mmx) based on adictionary with more than 20, 000 Myanmar lexicon entries.
The token numbers of the two schemes are listed in the myrows in the tables (syl / mmx). Due to multi-syllable tokens, the syl has larger token numbers than mmx. We show
a simple segmentation example in Fig. 4. 4
Table 1: Training Corpus.
Lang.
my
zh
en
fr

Sentences
155, 121
155, 121
155, 121
155, 121

Tokens (syl / mmx for my)
1, 835, 687 / 1, 508, 234
1, 062, 809
1, 161, 283
1, 248, 764

Table 2: Development Data.
Lang.
my
zh
en
fr

Sentences
5, 0005, 0005, 0005, 000Tokens (syl / mmx for my)
59, 058 / 48, 546
34, 10337, 49640, 256

Table 3: Test Data.
Lang.
my
zh
en
fr

Sentences
2, 0002, 0002, 0002, 000Tokens (syl / mmx for my)23, 661 / 19, 425
13, 799
15, 146
16, 173

4 As we have mentioned, original Myanmar morphemes are monosyllabic
and there are polysyllabic morphemes of loanwords. Actually, “word” is nota clear (and natural) unit in Myanmar sentence. In mmx scheme, we havepolysyllabic words not only derived from polysyllabic morphemes, but also
derived from fixed patterns of monosyllabic morphemes, as Fig. 4 shows.

syllableတကျးဇူူ း ေင်

max-matching်တကျးဇူူ းေငူ

morphologicalတကျးဇူ
ူး

ေင်

ပါ

ေယ်

ပါ

ေယ်

ပါ

ေယ်

Figure 4: Segmentation example of a Myanmar expression,
meaning “thank you”. The two upper rows are the syllablebased segmentation, where each box contains a syllable, anddictionary-based maximum matching, where the first threesyllables are merged. The lower row illustrates a morphologically oriented analysis, where the first two syllables shouldbe merged. The meanings of four boxes in the lower row
are approximately: “gratitude”, “put”, polite marker, andsentence-ending marker.
For the source-side language parsing, we use the Stanford
dependency parser5 for Chinese and English parsing [13, 14].
We use the Stanford tagger6 [15] for French tagging (CC tag
set [16]) and Malt parser7 [17] for French parsing. LADER8
is used to realize the unsupervised approach in [7] as a comparison approach. For the model training in LADER, we
randomly sample 1, 000 automatically aligned sentence pairs
from training set because we do not have manually-aligned
data. Table 4 of [7] shows that increasing the training data
for LADER from 600 to 10, 000 automatically aligned sentence pairs only brought a gain of 0.1 – 0.2 BLEU, therefore
we considered a training set size of 1, 000 to be sufficient9 .
We use the phrase-based (PB) SMT system in Moses10[2] as a baseline system. GIZA++11 [18] is used to alignword and alignment is symmetrized by grow-diag-final-andheuristics [1]. The lexicalized reordering model is trainedwith the msd-bidirectional-fe option [19]. The maximum
phrase length is 7. We use SRILM12 [20] to training 5-gram
language model with interpolated modified Kneser-Ney discounting [21] on Myanmar training data.
In decoding, we adopt the default settings of the Moses
decoder except the distortion-limit (DL). That is, ttable-limit
is 20 and stack is 200. We use DL of 0, 6, 12, and ∞ in
the experiments to analyze the reordering abilities of the preordering and the SMT reordering. We tuned the parameterweights on the development sets by MERT [22] and evaluated the translation on test sets by using two automatic measures: BLEU [23] and RIBES [24]. Identical decoding settings were applied on both development sets and test sets.
5 http://nlp.stanford.edu/software/lex-parser.
shtml
6 http://nlp.stanford.edu/software/tagger.shtml
7 http://www.maltparser.org/index.html
8 http://www.phontron.com/lader/
9 The training of LADER usually takes long time. Under the default settings of LADER, 500 iterations on 1, 000 sentences with 32 threads took
more than 10 hours for each translation task in our experiment.
10 http://www.statmt.org/moses/
11 https://code.google.com/p/giza-pp/
12 http://www.speech.sri.com/projects/srilm/

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Table 4: Test set BLEU / RIBES of zh-my.syl.
DL
0
6
12
∞

Baseline
35.5 / .817
37.9 / .831
38.5 / .832
38.4 / .834

LADER
36.2 / .816
37.9 / .830
37.9 / .830
38.3 / .831

Table 10: Test set BLEU / RIBES on syl of zh-my.mmx.

Head Final.
38.5 / .835
38.7 / .832
38.8 / .832
38.6 / .832

DL
0
6
12
∞

Table 5: Test set BLEU / RIBES of en-my.syl.
DL
0
6
12
∞

Baseline
40.4 / .789
45.7 / .842
48.8 / .873
49.3 / .875

LADER
47.8 / .861
49.2 / .874
49.6 / .878
49.6 / .877

Baseline
36.8 / .786
40.9 / .825
45.1 / .861
45.7 / .862

LADER
43.9 / .852
45.2 / .859
45.5 / .859
45.7 / .857

Head Final.
47.8 / .870
49.9 / .885
50.3 / .886
50.2 / .882

DL
0
6
12
∞

Baseline
32.9 / .799
34.9 / .816
35.5 / .817
35.2 / .816

LADER
34.6 / .810
35.1 / .816
35.7 / .817
35.6 / .814

DL
0
6
12
∞

Baseline
40.4 / .802
44.7 / .835
48.6 / .873
49.0 / .876

LADER
48.0 / .867
48.9 / .871
49.5 / .877
49.5 / .875

Head Final.
47.8 / .871
49.0 / .881
49.8 / .880
49.7 / .878

Baseline
36.9 / .791
39.7 / .818
44.3 / .855
44.7 / .856

LADER
43.6 / .844
44.7 / .852
45.1 / .852
45.4 / .853

LADER
51.2 / .879
52.1 / .883
52.8 / .887
52.8 / .887

Head Final.
51.5 / .882
52.7 / .891
53.4 / .890
53.2 / .889

Baseline
40.5 / .803
43.1 / .831
47.5 / .867
47.8 / .867

LADER
47.0 / .857
48.0 / .865
48.0 / .864
48.4 / .865

Head Final.
46.6 / .860
47.9 / .869
48.4 / .870
48.3 / .865

5.3. Discussion

Table 9: Test set BLEU / RIBES of fr-my.mmx.
DL
0
6
12
∞

Baseline
45.0 / .814
48.6 / .847
52.0 / .882
52.5 / .885

ferent distortion limits (DLs). The best BLEU scores among
the different DLs are underlined and bold BLEU scores are
significantly different (p < 0.05) to the best baseline BLEU
score. As the log-linear model weights were tuned to optimize the BLEU rather than the RIBES score on the development sets with MERT, the RIBES scores shown in the tables
are only a complementary evaluation of translation performance on word order.
In Tables 4 – 6, the evaluation is on syl and in Tables 7 –9, on mmx. So the results in the corresponding tables of these
two groups are not comparable. In Tables 10 – 12, we show
the results on syl for mmx outputs. So, the corresponding
results in Tables 4 – 6 and Tables 10 – 12 are comparable.

Head Final.
35.4 / .811
36.5 / .821
36.5 / .819
36.5 / .820

Table 8: Test set BLEU / RIBES of en-my.mmx.
DL
0
6
12
∞

Head Final.
38.5 / .829
39.7 / .838
39.9 / .838
39.6 / .838

Table 12: Test set BLEU / RIBES on syl of fr-my.mmx.

Head Final.
43.7 / .850
45.6 / .860
46.5 / .866
46.5 / .860

Table 7: Test set BLEU / RIBES of zh-my.mmx.
DL
0
6
12
∞

LADER
38.0 / .829
39.0 / .835
39.4 / .835
39.2 / .832

Table 11: Test set BLEU / RIBES on syl of en-my.mmx.

Table 6: Test set BLEU / RIBES of fr-my.syl.
DL
0
6
12
∞

Baseline
36.8 / .818
38.4 / .836
39.0 / .837
38.6 / .833

Head Final.
43.6 / .847
44.9 / .855
45.4 / .855
45.3 / .853

5.2. Results
We list the experimental results of three source languages(zh, en, fr) with two target Myanmar segmentation
schemes (my.syl, my.mmx) in Tables 4 – 12. In each table,
two evaluation measures (BLEU / RIBES) are given with dif-

In Tables 1 – 3, it can be observed that the average sentence length of the corpus used is quite small (all less than
10 except for my.syl). This is because the corpus mainlycontains colloquial, rather than literary sentences. This bias
suggests two problems. First, the state-of-the-art Moses system can handle the reordering well for short sentences, wherea pre-ordering approach may not show its power. Second,
there may be more errors in parsing colloquial sentences thanliterary ones, which may reduce the performance of rulebased head finalization.
Using the same analysis as in [4], first we calculate theaverage Kendall’s τ on the training sets (Table 13) to investigate the reordering performance. We observed the following
phenomena:

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Table 13: Average Kendall’s τ on training sets.
Language Pair
zh-my.syl
en-my.syl
fr-my.syl
zh-my.mmx
en-my.mmx
fr-my.mmx

Baseline
.69
.53
.53
.69
.53
.54

LADER
.79
.79
.76
.80
.79
.76

Head Final.
.83
.79
.75
.83
.79
.76

• The two different segmentation schemes of Myanmar
lead to very similar average Kendell’s τ .
• LADER can produce an average Kendall’s τ of around.75 – .80 irrespective of the value of average Kendall’sτ in its input corpus.
• Dependency-based head finalization shows identical
performance to LADER in en-my and fr-my, butbetter performance on zh-my, where the corpus before pre-ordering already has a relatively high average
Kendall’s τ .
From Table 13, it is noticeable that en-my and fr-myhave nearly identical characteristics while zh-my is different
from them. This phenomenon is reflected in the evaluation
results on the test sets.
In zh-my translation, we find LADER hardly improves
performance over the baseline SMT system in both syl andmmx, while the head finalization approach improves performance over the baseline in both cases and more substantially for mmx. LADER has higher performance on en-myand fr-my, and the proposed head finalization technique
has identical or better performance. Since the difference inword order is not as severe for zh-my as for en-my andfr-my (as indicated by the Kendall’s τ statistics), we consider rule-based head finalization to be a better complementary approach for the SMT system for zh-my. For language
pairs with considerably different word orders as en-my andfr-my, LADER and rule-based head finalization, despite
their essentially different mechanisms, attain similar levelsof performance.
In Tables 4 – 12, it can also be noticed that the differences
are quite large between DL = 0 (i.e. monotone translation)and the corresponding best BLEU in each baseline result, but
the differences are reduced by both pre-ordering approaches.
So, the performance gains over the baseline by using preordering diminish as the DL is increased. As to the RIBES
score, the differences actually are not substantial between thebaseline, LADER, and the head finalization approach. We
consider these to be reasonable phenomena caused by theshort length of the sentences in the corpus.
A major factor affecting the performance of the rulebased head finalization approach is the precision of the parser
used, and perhaps the most important factor affecting theperformance of a statistical approach, such as LADER, is

the quality of the training data. In the survey conducted in[25], they reported “we observed relatively small effects onreordering quality in response of parsing errors”. We visually inspected a sample of the parsing results used in our
experiments and found parsing errors did not have a large
effect on the performance of our head finalization approach.
We consider a major benefit of our approach is that we almost
always use the “head” information from a dependency parse,
which leads to robustness. The performance of LADER is
greatly affected by the quality rather than the amount. So
it is sensitive to the nature of languages involved, and also
to their word segmentation schemes because they affect thequality of word alignment used to train LADER.
Among the various segmentation schemes for Myanmar,
we believe the syl strategy has a tendency to over-split sentences and mmx may lead to some long expressions without
necessary splits as illustrated in Figure 4. It can be seen thatthe data segmented using mmx has fewer tokens and relatively longer words. It was expected that the the evaluation
scores in Table 7 – 9 would be lower than those in Table 4 –6. Conversely, if the translation is done on mmx and evaluated by syl, as shown in Table 10 – 12, we find the results
are better than those in Table 4 – 6. The experimental results
show that the mmx strategy is a better segmentation strategy
than syl. Although mmx introduces long expressions, it canoffer more meaningful units in word alignment and translation, which lead to a better performance. However, a more
useful standard morpheme analysis system should hopefully
be built for Myanmar in the future.
We show translation examples of zh-my, en-my andfr-my. The examples are selected from the best results of
mmx and illustrated using syl segmentation. It can be seenthat the head finalization has a rigidity with respect to thesyntactic structure. For example, the objects of verbs arestrictly arranged in front positions in head finalization (actually, untouched), such as the Chinese “ 我 ” in Fig. 5, theEnglish “i” in Fig. 6, and the French “j’ ” in Fig. 7. Whilein the pre-ordering from LADER, those words are scattered.
For example, in the first example of Fig. 6 and in Fig. 7, the“i” and “j’ ” are moved to the end of the sentences. This isbecause LADER does not have information on the syntactic
structure of a sentence. In this example, LADER moves thephrases “i want” and ”j’ ai” as whole units to the sentence
ends, and makes further local swapping within the phrases.
The second example of Fig. 6 shows the simplicity of ourhead finalization approach; in this example, only the verb
“bring” is moved to the end of the sentence.

6. Conclusion and Future WorkIn this paper, we conducted pre-ordering experiments on
Chinese-, English-, French-to-Myanmar translation. We
found that a simple dependency-based head finalization preordering strategy can consistently and efficiently improve abaseline SMT system. The proposed head finalization approach does not require parallel training data, and only de-

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Baseline Input 对不起 ， 请 告诉 我 这个 怎么 用 ？ (sorry , please tell me this how use ?)LADER Input 这个 对不起 ， 请 告诉 我 怎么 用 ？ (this sorry , please tell me how use ?)Head Final. Input 对不起 ， 我 这个 怎么 用 告诉 请 ？ (sorry , me this how use tell please ?)Baseline Output ေ ဆိ ေ် တောက်

တကျး ဇူ း ပပ ပပီ း ဒီ ဟာ ကို ဘယ် ေို သု ံ း ရ မ ေဲ ။

LADER Output တကျး ဇူ း ပပ ပပီ း ေ ဆိ ေ် တောက် ။ တကျး ဇူ း ပပ ပပီ း ဒီ ဟာ ကို ဘယ် ေို သု ံ း ရ မ ေဲ ။
Head Final. Output ေ ဆိ ေ် တောက်

Reference ေ ဆိ ေ် တောက်ဒါ ကို ဘယ် ေို သု ံ း ရ မ ေဲ ဆို ော တ ပာ ပ တပး နို င် မ ေား ။ဒါ ကို ဘယ် ေို သု ံ း ရ ေယ် ဆို ော ပ တပး ပါ ေား ။

Figure 5: Chinese-to-Myanmar translation example. (For the input rows, word-by-word English literal translations are annotatedin gray. An unconstrained translation of the original Chinese sentence is “Excuse me, could you tell me how this works?” )Baseline Input i want to send this parcel to japan .

please bring me some ice .

LADER Input this parcel to japan send to want i .

ice some me please bring .

Head Final. Input i this parcel japan to send to want .

please me some ice bring .

Baseline Output ဒီ အ ထု ေ် ကို ဂျ ပန် ကို ပို ့ ချင် ေို ့ ပါ ။ တရ ခဲ ကို နည်း နည်း တောက် ယူ ော တပး ပါ ။

LADER Output ဒီ ပါ ဆယ် ဂျ ပန် ကို ပို ့ ချင် ပါ ေယ် ။
Head Final. Output ဒီ ပါ ဆယ် ဂျ ပန် ကို ပို ့ ချင် ေို ့ ပါ ။တရ ခဲ နည်း နည်း ယူ ော တပး ပါ တနာ် ။တရ ခဲ ယူ ော တပး ပါ ။

Reference ဒီ ပါ ဆယ် ဂျ ပန် ကို ပို ့ ချင် ေို ့ ပါ ။တရ ခဲ ကို ယူ ော တပး ပါ ။

Figure 6: English-to-Myanmar translation examples.
Baseline Input j' ai oublié mon billet d' avion . (I have forgotten my ticket of aeroplane .)LADER Input avion d' billet mon oublié ai j' . (aeroplane of ticket my forgetten have I .)Head Final. Input j' mon avion d' billet oublié ai . (I my aeroplane of ticket forgetten have .)Baseline Output ကွန် တော့် ရဲ ့ တေ ယာဉ် ေက် မှ ေ် တမ့ ကျန် ခဲ့ ေယ် ။

LADER Output တေ ယာဉ် ေက် မှ ေ် တမ့ ကျန် ခဲ့ ေယ် ။
Head Final. Output တေ ယာဉ် ေက် မှ ေ် တမ့ ကျန် ခဲ့ ပါ ေယ် ။

Reference တေ ယာဉ် ေက် မှ ေ် တမ့ ကျန် ခဲ့ ပါ ေယ် ။

Figure 7: French-to-Myanmar translation example. (For the input rows, word-by-word English literal translations are annotatedin gray. An unconstrained translation of the original French sentence is “I forgot my airline ticket.” )pends on a source-side dependency parser, which allowed
it to attain higher performance than an unsupervised baseline in our experiment. The simplicity and efficiency of the
proposed head finalization approach should allow it to findpractical application on large scale data sets.
In further work, we plan to expand the parallel data andconduct experiments on larger corpora. We are also developing a morpheme analyzer and parsers for Myanmar to facilitate the transference of more techniques of Japanese andKorean language processing to Myanmar language processing.

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A. Head Finalization for Chinese
We use the Standford Chinese dependency parser.
• The conj arc is used to identify coordination.

• The punct arc is used to identify punctuation marks.

• The asp, assm, ba, cop, cpm, dvpm, mmod arcs aretaken as auxiliary verbs or post-positioned particles.
They are always arranged after their heads.
• The neg arc is used to identify the negation.

• We clean up parsing errors around several common
Chinese function words, to insure:– sentence final particles “ 啊 ”, “ 吧 ”, “ 的 ”,
“ 了 ”, “ 吗 ”, “ 呢 ”, “ 呀 ” are always after theirhead words;– determiners “ 这 ”, “ 那 ”, “ 哪 ” are always before their head words.
• The article deletion process is not applied in Chinese.

B. Head Finalization for English
We use the Standford English dependency parser.
• The conj, cc arcs are used to identify coordination.

• The punct arc is used to identify punctuation marks.

• The aux, auxpass, cop arcs are taken as auxiliary
verbs. They are always arranged after their heads.
• The mark arc and “when”, “where” with advmod arc
are always arranged after their heads.
• The neg arc is used to identify the negation.

• The “there be” of an existential clause is kept together.

• For the process of article deletion, we delete “a”, “an”,“the”.

C. Head Finalization for French
We use Malt French parser with the CC tag set.
• The *coord* arcs are used to identify coordination.

• The ponct arc is used to identify punctuation marks.

• The *aux* arcs are taken as auxiliary verbs. They arealways arranged after their heads.
• The “ne”, “n’ ” with mod arc is used to identify thenegation.
• The “il y a” and “y a-t-il” of an existential clause is
kept together.
• For the process of article deletion, we delete “le”, “la”,“l’ ”, “les”, “un”, “une”.

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