System Description: Dependency-based Pre-ordering forJapanese-Chinese Machine Translation
Jingsheng Cai Yujie Zhang Hua Shan Jinan Xu
School of Computer and Information Technology, Beijing Jiaotong University{12120397, yjzhang, 13120422, jaxu}@bjtu.edu.cn

Abstract(Wang et al., 2007), etc. and shown its effectiveness. In recent years, more and more pre-orderingresearch used dependency parsing for the translation of Arabic-English (Habash, 2007), EnglishSOV languages (Xu et al., 2009) and ChineseEnglish (Cai et al., 2014), because the accuracyof dependency parsing is greatly improved.

This paper describes the Beijing Jiaotong University Japanese-Chinese machine translation system which participated in the 1st Workshop on Asian Translation (WAT 2014). We propose a preordering approach based on dependency
parsing for Japanese-Chinese statisticalmachine translation (SMT). Our systemachieves a BLEU of 24.12 and a RIBES of79.48 on the Japanese-Chinese translation
task in the official evaluation.

1This paper introduces a Japanese-Chinese SMT
system which employs a pre-ordering approachbased on dependency parsing. Since Japanese isa language with a fairly free word order, dependency parsing can describe the relation between
two Japanese cases in a sentence better than constituent parsing. Therefore, we adopt dependency
parsing for pre-ordering. Experimental results
show that our approach can improve the BLEU
score on the test set by 0.18, compared with thebaseline system (without pre-ordering).

Introduction

Difference in word order between source language
and target language will cause troubles in wordalignment and further affect the quality of statistical machine translation (SMT). Therefore, it becomes an issue in SMT, especially in the language
pairs where there are great difference in word order between source language and target language.
Syntax-based pre-ordering has demonstrated effectiveness in previous research. These kinds ofapproaches first parse the sentences in the side ofsource language. Then pre-ordering rules are applied to the created parse trees, in order to obtain source language sentences which have similar word order with target language sentences. Finally, the reordered source language sentences areused in the SMT system, not only in training, butalso in tuning and translating. In other words, allof the sentences in the training set, the development set and the test set should be reordered while
applying these kinds of approaches.
Since parsing is required in pre-ordering approaches, two popular parsing are often considered, i.e. constituent parsing and dependencyparsing. Constituent parsing has been employedin pre-ordering for the translation of EnglishFrench (Xia and McCord, 2004), GermanyEnglish (Collins et al., 2005), Chinese-English

Section 2 describes some issues in JapaneseChinese translation and proposes our dependencybased pre-ordering approach. Section 3 reports
on our experiment results on a Japanese-Chinesephrase-based SMT (PBSMT) system. Section 4
concludes this paper.

2

Dependency-based Pre-ordering
Approach

Japanese is a kind of SOV language and Chinese
is a kind of SVO language. Therefore, great difference exists in their word order. Moreover, bothof them have very free word order, i.e. a sentence
may have several expressions by only changing its
word order. This fact causes troubles while translating Japanese into Chinese.
This section describes some issues in the translation of Japanese-Chinese and then proposes ourdependency-based pre-ordering approach to tackle
these problems.
39

Proceedings of the 1st Workshop on Asian Translation (WAT2014), pages 39‒43,Tokyo, Japan, 4th October 2014.

Figure 1: An example Japanese sentence and its
Chinese counterpart, along with their word alignment.

Figure 3: Dependency parse tree for the Japanese
sentence in Figure 1.
“に” is aligned to the Chinese preposition “在”,which is a cross word alignment within the “ni”
case (“どこの会社に”). Erroneous word alignments often appear in such kinds of sentences.
2.2

Figure 2: An example with a cross word alignment, owing to the existence of a Japanese particle
“に”.
2.1

Dependency-based Pre-ordering

After investigating the issues in Japanese-Chinesetranslation, we attempt to tackle them by introducing a pre-ordering approach into the PBSMT
system. Considering that current Japanese dependency parsing is of high accuracy and provides case information, we think these conditions
are suitable for both global and local pre-ordering
operations. We therefore propose a dependencybased pre-ordering approach.
Figure 3 shows the dependency parse tree of theJapanese sentence in Figure 1. There are threecases in this sentence. Both of the “ga” case andthe “wo” case depend on the last phrase called
as “bunmatu” case, i.e. the ROOT. According to
the dependency tree, we can easily move the “wo”
case after the “bunmatu” to obtain an SVO word
order sentence. After operating this pre-ordering,
the Japanese sentence can be translated into “我/I读/read 书/book 。/.” in Chinese, whose word order is the same as its Chinese counterpart.
We investigate the phenomena of word order exchanging, such as “wo” case and “ni” case, on
the ASPEC Japanese-Chinese paper excerpt corpus1 . We take statistics of the various cases andobserve the samples of the cases with high frequency. Based on the observed results, we build
pre-ordering rules, which consist of global preordering and local pre-ordering.
Inspired by the work of Xu et al. (2009), we obtain the global pre-ordering rules in the following
way. We focus on verbs and classify the cases that
directly depend on verbs into several groups. We
then set sequence numbers to the groups by considering the word orders of their Chinese translations in a Chinese sentence, as we observe fromIssues in Japanese-Chinese Translation

As is known to all, Japanese is a kind of SOV language, in which the verb (V) occurs after the object (O), and Chinese is a kind of SVO language,in which the verb (V) occurs before the object(O). This is the most common difference between
Japanese and Chinese. Figure 1 shows an exampleof a Japanese sentence and its corresponding Chinese sentence with their word alignment. Thesesentences means “I read book.”. As describedin above, the verb “read” occurs after the object“book” in Japanese but before the object in Chinese. This kind of phenomena is often observed inJapanese-Chinese parallel corpus, which unavoidably brings about wrong results word alignment
and further affect the quality of machine translation.
Japanese is a kind of agglutinative language
but Chinese is not. A Japanese sentence consistsof phrases which are usually ended with a particle, indicating the case of the phrase. Although
all Japanese particles are not content words, theparticles are often incorrectly aligned with Chinese content words in conventional word alignment. To relieve this problem, we consider alignment to connect the particles with Chinese prepositions. Figure 2 shows such an example, of which
the Japanese sentence consisting of four phrases- “wa” case, “no” case, “ni” case and “bunmatu”
case. It means “What company do you work for?”in English. The tokens within one phrase are displayed by one underline. The Japanese particle

140

http://lotus.kuee.kyoto-u.ac.jp/ASPEC/Group123(a) A Japanese sentence and its Chinese counterpart, alongwith their word alingment.

45Casega/が wa/はrenyou/conjunction
de/で ni/に he/ヘ kara/からmade/まで yori/より to/とbunmatu/文末wo/をTable 1: Groups of the cases and their sequencenumbers.

(b) Dependency parse tree for the Japanese sentence.

Figure 4: An example of pre-ordering operation
on a Japanese sentence.

into “研讨了/discussed 这些/these 范畴/category
包含/conclude 要素/element 数/number 。/.” in
Chinese, whose word order is more similar to its
Chinese counterpart.

the corpus. Table 1 lists the groups and their sequence numbers.
We first define the subtree of a case as the phrase
ended with the particle and those phrases that directly or indirectly depend on this phrase. Then
we design pre-ordering operation. For a given
Japanese dependency tree, if its root node contains a verb, conduct pre-ordering as the following
steps.
Step 1. Global Pre-ordering: Change the orderof the subtrees of the cases directly depend on the“bunmatu” case and the “bunmatu” case itself, according to the sequence number of the groups thatthey belong to, as listed in Table 1. The cases ofGroup 1 should occur before those of Group 2,and so forth. Note that if any two cases belong
to the same group, we keep their relative order unchanged.
Step 2. Local Pre-ordering: For “wo” case, “de”case, “ni” case, “he” case, “kara” case and “made”
case, move the particles to the front of the wholesubtree.
Figure 4 gives an example of conducting preordering operation. Figure 4 (a) displays theJapanese sentence to be reordered, the corresponding Chinese translation and the word alignment
between them. The English meaning is “(We) discussed the number of the elements that are contained in these categories.”. The dependency treeof the Japanese sentence is shown in Figure 4 (b).
First, global pre-ordering is conducted. The subtree of the “wo” case, i.e. all of the tokens occurring before “を”, is moved to the position after the“bunmatu” case. Then, local pre-ordering is conducted. The particle “を” and “に” are moved tothe front of the subtree of their cases, respectively.
The reordered Japanese sentence can be translated

3Experiments

Section 2 describes our dependency-based preordering approach for the translation of JapaneseChinese. This section reports on our experiments
and evaluation results.
We use MOSES PBSMT system (Koehn et al.,2007) in our experiments and use BLEU scores(Papineni et al., 2002) and RIBES score (Isozakiet al., 2010) for evaluation.
The data sets are from the ASPEC JapaneseChinese paper excerpt corpus. The training datacontains 672,315 sentences, the development datacontains 2,090 sentences and the test data contains
2,107 sentences.
We use a bilingual-based approach proposed inSu et al. (2013) for Chinese word segmentation,in which n-gram feature from the raw Chinesepart and word alignment from the parallel corpus
are introduced to augment the conventional model
based on annotation data.
We employ the KNP parser2 for Japanese dependency parsing. The KNP parser can create dependency tree for a Japanese sentence and provide the part-of-speech (POS) for each word, bothof which are used in our dependency-based preordering approach.
After Japanese dependency trees are obtained,
we conduct pre-ordering on the training set, thedevelopment set and the test set by applying thepre-ordering rules described in Section 2.2. The
reordered data sets are then used in training, tuning
and test in the Japanese-Chinese PBSMT system.
On the other hand, data sets without pre2

41

http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?KNP

SystemBaseline
Pre-ordering

BLEU on Dev
21.55
21.71

BLEU on Tst
20.71
20.89

RIBES on Tst
77.34
77.67

Table 2: Comparisons of the pre-ordering system and the baseline system.
Category
GlobalLocal

Positive
115
63

Unchanged
49
108

Negative
36
29

Table 3: Human evaluation for 200 reordered sentences, respectively.
same. Local pre-ordering helps improve the wordalignment in 31.5% sentences and 54% stays thesame. The results access the accuracy and effectiveness of our approach. Note that the “Unchanged” local pre-ordering is 108, which is overhalf of all. The reason for this is that in fact manyJapanese particles have no alignment relation with
any Chinese word, pre-ordering of these particles
will not change the number of cross alignment atall.

ordering are used in our baseline system for comparison.
Table 2 presents the evaluation results of thepre-ordering system and the baseline system, including the BLEU scores on the development set,the BLEU scores and the RIBES scores on the testset. The results show that the system employing
our dependency-based pre-ordering approach outperforms the baseline system slightly, both on thedevelopment set and the test set. Note that theseresults are different with those in the official evaluation because we should keep the consistency
in Chinese word segmentation, while the officialevaluation uses other word segmenters (kytea andStanford).
To access the accuracy of our pre-ordering approach, we also conduct human evaluation for thereordered sentences. 200 global reordered sentences and 200 local reordered sentences are extracted from the training data, along with theircorresponding Chinese sentences. For global preordering, we align the Japanese cases with theChinese phrases. For local pre-ordering, we pickthe reordered Japanese cases and align the words
in them with the Chinese words. For both categories of pre-ordering, we count the number ofcross alignment and compare it with the one ofthe original sentence pairs. Table 3 shows the results of our human evaluation. Here, “Positive”
means that the number of the sentences in which
the cross alignment decreases after applying thepre-ordering, “Negative” means that the numberof the sentences in which the cross alignment increases after applying the pre-ordering, and “Unchanged” means that the number of the sentencesin which the cross alignment stays the same after applying the pre-ordering. As shown in Table 3, global pre-ordering helps improve the wordalignment in 57.5% sentences and 24.5% stays the4Conclusion

This paper describes the Beijing Jiaotong University Japanese-Chinese PBSMT system participated in WAT 2014. The system employs adependency-based pre-ordering approach. The approach conducts two categories of pre-ordering:1) global pre-ordering reorders the cases directly
depending on the verb of “bunmatu” case; 2)local pre-ordering moves the Japanese particles
to the front of the case they belong to. Experimental results show that our pre-ordering approach improves the BLEU score by 0.18 and theRIBES score by 0.34 on the test set of the ASPEC Japanese-Chinese corpus. The accuracy andeffectiveness of this approach are also accessed byhuman evaluation.
Note also that some cases which are not listed
in Table 1 may also be parsed to depend on a verbof “bunmatu” case. We do not operate any preordering in such situation currently because theirfrequency is very low and it is difficult to summarize their reordering phenomena.
Our research builds up a framework forJapanese-Chinese pre-ordering. However, the improvement for the PBSMT system is not as greatas our expectation. As shown in Table 3, some
negative results are brought into the word alignment after pre-ordering. This implies that the there42

are some problems in current pre-ordering rules.
More features from dependency tree may be introduced as constrains to the pre-ordering approachin the future.

Chao Wang, Michael Collins, and Philipp Koehn.
2007. Chinese syntactic reordering for statisticalmachine translation. In Proceedings of the 2007
Joint Conference on Empirical Methods in Natural
Language Processing and Computational Natural
Language Learning, pages 737-745.

Acknowledgments

Fei Xia and Michael McCord. 2004. Improving
a statistical MT system with automatically learned
rewrite patterns. In Proceedings of Coling 2004,
pages 508-514.

This work is supported by Key Lab of Intelligent Information Processing of Chinese Academyof Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China and International Science & Technology Cooperation Program of China (Grant No. 2014DFA11350).

Peng Xu, Jaeho Kang, Michael Ringgaard, and Franz J.
Och. 2009. Using a dependency parser to improve
SMT for subject-object-verb languages. In Proceedings of HLT-NAACL, pages 245-253.

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