Parsing Myanmar (Burmese) by Using Japanese as a Pivot
Chenchen Ding, Ye Kyaw Thu, Masao Utiyama, Eiichiro Sumita
Multilingual Translation Laboratory, NICT
3-5 Hikaridai, Seikacho, Sorakugun, Kyoto, 619-0289, Japan{chenchen.ding, yekyawthu, mutiyama, eiichiro.sumita}@nict.go.jp

Abstract
As Myanmar (Burmese) and Japanese sharesimilar syntactic structures, we explore an approach for parsing Myanmar by using Japanese
as a pivot. Specifically, we first apply a statisticalmachines translation (SMT) system to translate
Myanmar sentences into Japanese, and then weparse the Japanese translations. Finally, the Japanese syntactic structures are mapped to the original Myanmar sentences. As a state-of-the-art
SMT system performs well on translating syntactically-similar languages and the parsing techniques for Japanese have been well-studied, we
have satisfactory results in experiments.

1. Introduction
Syntactic parsing is one of the basic tasks innatural language processing (NLP), focusing on
revealing the syntactic structures of sentences belong to particular natural languages. As the structures of natural languages have much more ambiguities than artificial programing languages, theparser of natural languages cannot be easily realized by simple rules and deterministic algorithms.
Instead, annotated corpora (i.e., tree-banks) anddata-driven statistical approaches are studied andapplied for academic researches and practical applications. As the construction of annotated corpus costs much in both time and money, it turns to
be a long-term project, especially for those under-studied, low-resource languages, such as Myanmar.
In this paper we explore an approach for parsing Myanmar without annotated Myanmar corpus,
but use Japanese, a well-studied language, as apivot. The motivation of this work based on threefacts: 1) Japanese and Myanmar have quite similar syntactic structures; 2) we have considerable
Japanese-Myanmar parallel data and the state-ofthe-art statistical machine translation (SMT) techniques can give satisfactory translation performance between the two languages; 3) the available Japanese morphological analysis tool and parser can give satisfactory results. According to theabove-mentioned points, the specific process ofparsing Myanmar sentence in this paper is composed of three steps: 1) to translate a Myanmar
sentence in to Japanese sentence using a trained
SMT system; 2) to parse the Japanese sentence; 3)
to map the structure of the Japanese sentence into
the original Myanmar sentence. For step 1) and 2),
off-the-shelf SMT system and Japanese processing tools are used. For step 3), we propose a
simple approach to establish the correspondence
structures between Japanese and Myanmar.
In order to examine the performance of the
proposed approach, we conducted experiments on
the basic travel expressing corpus (BTEC) withmanual checks on samples from a test set. We
mainly focus on evaluation of two aspects: 1) thecorrectness of the parsing on the Japanese translation, and 2) the correctness of the mapped struc-ture on the original Myanmar sentence. The experimental results illustrate, that we can actuallyobtain relatively correct syntactic structures on
Myanmar sentences by using Japanese as a pivot.

2. Proposed Approach
As mentioned, the process of translating Myanmar sentences into Japanese and parsing Japanese are only using off-the-shelf tools, we mainly
describe the mapping process of establishing thefinal Myanmar sentence structure in this section.
The specific settings and details of SMT and Japanese processing will be presented in the following section of experiment.
Because Japanese and Myanmar are typicalhead-final languages with abundant case-markers
and particles to illustrate the relations between
phrases within a sentence, their word orders are
relatively free as long as the head-final restriction
is satisfied. Consequently, dependency relation on
chunk-level is a more suitable grammatical formulation than constituency-based analysis on thiskind of language. In this work, the Japanese sentences are first parsed to obtain their chunk dependencies, and then the chunks and dependency
between chunks are mapped to the Myanmar-side
successively. We describe the two mapping steps
in the following sub-sections respectively.

2.1. Chunk Mapping
Fig. 1 illustrates the process of chunk mapping
between Japanese and Myanmar. With the help of
token alignment between input and output generated by the SMT system, we can get a rough correspondence of chunks. Specifically, each chunkon Japanese-side is examined to figure out the corresponding cover range on the Myanmar-side.
Then we use two heuristic rules to refine the roughcorrespondence in order to get a clean chunk segmentation on the Myanmar-side. They are: 1) tomerge overlapped chunks, and 2) to appending
left-out words to it previous chunk. Rule 1) is aconservative process to handle the disagreementof chunk segmentation and translation on Japanese, which may be caused by alignment errors in
the SMT system or by inherent difference in expression between the two languages. We only prefer under-segmenting rather than over-segmenting for the Myanmar sentences. We further userule 2) to group unaligned words because they areusually functional particles positioned after thecontent word they are related to. Generally, thechunk mapping process from Japanese to Myanmar will reduce the number of chunks because wewant to ensure the Myanmar sentence not to beover-segmented.

Figure 1. Process of chunk mapping. Dots
stand for tokens and solid boxes stand forchunks. The first row stands for the Japanese
sentences; the second and third rows stand fororiginal Myanmar sentence. Between the firstand second rows, corresponding tokens intranslation are linked.

2.1. Dependency Mapping
Fig. 2 illustrates the mapping of dependency
relations after the mapping of chunks. Basically,
the head-modifier chunk pairs on Japanese-side
will be mapped to Myanmar-side directly. As

mentioned, the number of chunks may be reducedon the Myanmar-side. So, for 1) merged chunks,
only the dependency arc of the coresponding
right-most Japanese chunk will be retained andother related arcs are deleted; for 2) disappearedchunks on Japanese-side, all the dependency arcsrooted on them will be passed to their first
undisappeared ancester chunk, and all the arcs
pointed to them will be deleted. After the above
two modifications, we can obtain a deteminedmapping of the dependency arcs.

3,000 sentences respectively. The training and develop sets were used to train a standard phrasebased (PB) SMT system to translate Myanmar
into Japanese. The test set was used for testing andevaluating the Myanmar parsing approach proposed in this paper.
For the PB SMT, we used MOSES 1 [2] withdefault settings in training and decoding. The language model used in SMT was an interpolated
modified Kneser-Ney discounting 5-gram model,
trained on the Japanese part of the training set by
SRILM2 [3]. The Myanmar sentences were segmented into words using an in-house CRF-based
tool for the SMT system. For the output Japanese
sentences from the SMT system, we used MeCab3with IPA dictionary for segmenting and CaboCha4[4] for chunking and parsing.

3.2. Evaluation

Figure 2. Process of dependency mapping.
The meaning of dots and boxes are identical toFig. 1. The relation between chunks of the twosides is marked by gray arrows. The dependency relation of chunks are noted by arcs.

3. Experiment3.1. Data and Tools
We investigated the performance of our proposed approach using the basic travel expressing
corpus (BTEC) [1]. The corpus is composed ofthree sets of training, development, and test, containing 457,249 sentences, 5,000 sentences and
12First, we report the performance of the PB
SMT system on Myanmar-to-Japanese translation
used in experiments. We tested different setting
and the results are listed in Table 1.
Table 1. Performance of PB SMT on Myanmar-to-Japanese translation.
DL Lex.-Reo. BLEU RIBES
0no.81238.5
0
yes
38.2.8123
yes38.2.812
6
yes.814
38.5
The DL in Table 1 means the distortion-limit
used in decoding, and the column of Lex.-Reo.
shows whether the lexicalized orientation reordering is used. Two measures, the BLEU score [5]and RIBES [6] are reported in Table 1. We observed a purely monotone translation (DL=0)

http://www.statmt.org/moses/

3

http://www.speech.sri.com/projects/srilm/4

http://taku910.github.io/mecab/
http://taku910.github.io/cabocha/

without reordering model can give a good results
and a larger DL could hardly improve the performance, although using a DL of 6 may give aslightly higher RIBES. The result is reasonable
due to the similarity of the two languages. So, we
simply used the settings of DL=0 and no reordering model for the PB SMT system in experiments.
We evaluated the accuracy of chunking anddependency on both Japanese and Myanmar. Because the Japanese sentences are output by the PB
SMT system, sometimes they may become unnatural, which will cause the parsing error and finallyaffect the dependency mapping to Myanmar.
For the evaluation on chunks, we only concentrated on the part within the chunks to judge
whether they are meaningful. Hence, Japanesechunks with unknown words are all labeled “notcorrect”. On the Myanmar side, the chunks are
generally long phrases, which may cover different
clauses caused by the merging step in our approach. This kind of Myanmar chunks are also labeled as not correct. For the evaluation on dependency, the head-modifier pair of chunks are examined to see whether they have syntactic relations.
If a chunk is meaningless, either in the translated
Japanese and original Myanmar, the dependency
around it may also be wrong. However, some dependency results are correct on wrong chunks, because of the good performance of the parser.
Table 2. Chunk and dependency accuracy on
Japanese and Myanmar sentences.
JapaneseMyanmar
chunk 91.6% (97.3%)
95.7%
dependency
98.6%
95.8%
The percentage of chunk and dependency accuracy on 500 randomly sampled Japanese / Myanmar sentence pairs from the test set are listed inTable 2. The percentage in brackets of Japanesechunk does not take those chunk with unknown
words as “not correct”. As to the denominator of
the percentage, i.e., the number of chunks, theoriginal 500 Japanese sentences contain 1,604
chunks and after mapping to the Myanmar side, it
reduced to 1,344 chunks.

3.3. Discussion
From the evaluation results in Table 2, it canbe observed that the final chunk and dependency
accuracy is over 95%. The high numerical results
demonstrate the proposed approach actually generates reasonable paring results on Myanmar sentences. On the translated Japanese sentences, theunknown Myanmar words did not affect the approach much, because the Japanese parsing morerelies on those functional morphemes, which areusually translated well. A translation and mapping
example is given in Fig. 3.

5. Related and Future Work
To solve the parsing problem of low-resource
languages by sharing features with high-resource
language has been studied in recent research [7, 8].
However, the “low-resource languages” referenced in these studies are actually not really “lowresource” but only having less resource than those
English large data set. For example, in the workof [9], the Irish language was taken as a case-study
and referred to as a low-resource language; however, there are well-annotated Irish data whichwere used in this study. Hence, the Myanmar language is more proper to be referred to as “no-resource” because presently we still have no welldeveloped tree-bank for it, or even no refined partof-speech tag set to describe Myanmar.
Recently, a line of research attempts to model
different languages using a universal syntactic
formulation [10, 11]. We think this will be aproper framework for annotated corpus building
and sophisticated Myanmar-oriented processing,on which we are working presently.

Figure 3. Example of translation and mapping in our experiment. The part in dashed box on
Myanmar-to-Japanese SMT is a noise brought by the SMT system, which causes two Japanesechunks merged into one Myanmar chunk in the mapping step. Even though, the mapped dependency relation on Myanmar is correct because the Japanese genitive case-marker “の” haveno Myanmar corresponding translation in this example.

5. Conclusion
In this paper, we proposed an approach toparse Myanmar sentences by using Japanese as apivot. The chunk and dependency structures of aMyanmar sentence are mapped from its Japanesetranslation with the help of a PB SMT system anda Japanese parser. Experimental results on a basic
travel expression corpus were examined manuallyand illustrated the proposed approach performed
satisfactory. We hope the work reported in this paper can play an auxiliary role in the future Myanmar tree-bank construction.

Acknowledgement

We thank Dr. Win Pa Pa from NLP Lab., University of Computer Studies Yangon (UCSY) forher help in evaluating the experiment results.

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