Word Order Does NOT Differ SignificantlyBetween Chinese and Japanese††‡Chenchen Ding†‡• Masao Utiyama Eiichiro Sumita Mikio Yamamoto•†Multilingual Translation Laboratory,
National Institute of Information and Communications Technology
3-5 Hikaridai, Seikacho, Sorakugun, Kyoto, 619-0289, Japan‡
Department of Computer Science, University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan• {tei@mibel., myama@}cs.tsukuba.ac.jp{mutiyama, eiichiro.sumita}@nict.go.jp

Abstract

An effective rule-based approach, head finalization has been proposed for English-to-Japanesetranslation (Isozaki et al., 2012). The approachtakes 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 head-driven phrase structure grammar (HPSG) parser. Generally, the idea
can be applied to other SVO-to-Japanese translation tasks, such as its application in Chinese-toJapanese translation (Dan et al., 2012).
However, the head finalization cannot be applied on the reverse translation task, i.e. Japaneseto-SVO translation, which becomes a more difficult task. Specifically, Japanese-to-English translation has been studied and several rule-based prereordering approaches have been proposed, taking advantage of the characters of Japanese andEnglish (Komachi et al., 2006; Katz-Brown andCollins, 2008; Sudoh et al., 2011; Hoshino et al.,2013; Ding et al., 2014). A comparison of theseapproaches is reported in Ding et al. (2014).
Because both Chinese and English are SVO
languages, to transfer approaches of Japanese-toEnglish to Japanese-to-Chinese translation is anatural idea. Based on the framework of Dinget al. (2014), we propose dependency-based prereordering rules for Japanese-to-Chinese translation in this paper. Contrary to our expectations,from the experimental results on ASPEC-JC data,
we discover that the rule-based pre-reorderingcannot improve the Japanese-to-Chinese significantly as in the case of Japanese-to-English translation. In a further investigation, we find a basic reason is that the word order is actually similar between Chinese and Japanese. Chinese andEnglish, though both of them are SVO languages,
have very different properties.

We propose a pre-reordering approach forJapanese-to-Chinese statistical machinetranslation (SMT). The approach uses dependency structure and manually designed
reordering rules to arrange morphemes ofJapanese sentences into Chinese-like wordorder, before a baseline phrase-based (PB)
SMT system applied. Experimental resultson the ASPEC-JC data show that the improvement of the proposed pre-reordering
approach is slight on BLEU and mediocre
on RIBES, compared with the organizer’s
baseline PB SMT system. The approachalso shows improvement in human evaluation. We observe the word order does notdiffer much in the two languages, though
Japanese is a subject-object-verb (SOV)language and Chinese is an SVO language.

1Introduction

The state-of-the-art techniques of statistical machine translation (SMT) (Koehn et al., 2003;
Koehn et al., 2007) demonstrate good performanceon translation of languages with relatively similar word orders (Koehn, 2005). 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 (Isozaki et al., 2012).
To resolve the word reordering problem inSMT, a line of research handles the word reordering as a separate pre-process, which is referred aspre-reordering. In pre-reordering, the word order on source-side is arranged into the target-sideword order, before a standard SMT system is applied, on both training and decoding phases.
77

Proceedings of the 1st Workshop on Asian Translation (WAT2014), pages 77‒82,
Tokyo, Japan, 4th October 2014.

自分の妻さえ连自己的妻子

まだ伴れて行った也

事がないのです

没有

妻には带去过妻子

Figure 1: Japanese tends to use formal noun (in dash-linebox) to “wrap” a long clause.

2Character of Chinese and Japanese

3In Ding et al. (2014), they proposed three kinds ofverb, noun, and copula rules to arrange the order ofJapanese chunks1 , with further two rules of morphemes to achieve a more correct pre-reordering.
However, their system can not directly applied onJapanese-to-Chinese. Generally, Chinese sharesmuch more similar characters with Japanese thanEnglish does, as listed in follows.

解らないのです

不理解

我的意思

Figure 2: A nominative phrase in Japanese
turns to be an accusative phrase in Chinese.

Proposed Approach

We use three inter-chunk rules, classified by thecorresponding head morpheme in a head chunk.
They are formal-noun rule, stative-verb rule anddynamic-verb rule.
Formal-noun rule
Head-initialization is conducted. We only apply the rule to chunks with the formal noun
koto as its head morpheme.

• Chinese is head-final in noun phrases, just asJapanese.

Stative-verb rule
The modifier chunk with nominative casemarker ga is moved after the head chunk. We
only apply the rule to chunks with a verb headamong aru, iru, and dekiru as its head morpheme.

• Chinese has no clear boundary between verbs
and prepositions. The coverb and serial verb
constructions are common in Chinese, whichsomewhat like Japanese.
Although it seems that the pre-reordering forJapanese-to-Chinese may be easier than the caseof Japanese-to-English, there are factors turning
the case more complex, as listed in follows.

Dynamic-verb rule
The following modifier chunks are moved after the head chunk.
• with an accusative case-marker wo• with a quotation particle to• with a formal noun as a head morpheme• Chinese has a strong tendency to avoid long
attributes before nouns, while long attributesare acceptable in Japanese, especially for theformal nouns in Japanese (Fig. 1).

We apply the rule to chunks with a verb except the three verbs used in the stative-verb
rule, as its head morpheme.

• Between Chinese and Japanese, the transitiveverbs and intransitive verbs are not in accordance in some cases. Essentially, case frameof verbs are not marched well between thetwo languages (Fig. 2).

We also use extra/intra-chunk rules to arrange
certain functional morphemes.
Intra-chunk rule
Auxiliary verbs, except ta and copula morphemes, are moved before head morpheme
within a chunk.

According to the mentioned issues, the prereordering cannot be conducted by only seeing specific Japanese functional morphemes, suchas various case-markers, which are often usedin many Japanese-to-English pre-reordering approaches. We design three new inter-chunk rules
and modified the intra/extra-rules based on Ding
et al. (2014).
1

私の意味がExtra-chunk rule
Functional morphemes attached to nouns,
except genitive case-marker no and parallel markers, are moved before the governing
range of the chunk.

i.e. bunsetsu

78OriginalJapanese

８種類のＴＥＣ分解菌をStative-verb
Pre-reorderedJapanese

できた

Formal-noun

ことが純粋分離することができたDynamic-verb

純粋分離する

８種類のＴＥＣ分解菌をdeleted

能够Chinesedeleted纯粹分离8种

TEC分解菌Figure 3: Example of three inter-chunk rules: formal-noun rule, stative-verb rule and dynamic-verb
rule. The alignment between pre-reordered Japanese and Chinese is manually aligned. By the threerules, Morphemes of Japanese are arranged in a Chinese-like order.

OriginalJapanese

今後の対応を急がねばならない

隣のベッドのねばならない急が今後の対応を

必须加快

今后的に

隣のベッドの枕の上

在相邻

病床的枕头

上

deletedIntra-chunk

Chinese

上にExtra-chunk

Dynamic-verb
Pre-reorderedJapanese

枕の对策

Figure 4: Examples of Intra- and Extra-chunk rules. The left example shows an intra-chunk move withina verb chunk, leading to a more correct reordering of Japanese functional morphemes, where the underlined parts are corresponding translation. The right example shows an extra-chunk move. A Japanesepost-positioned case-marker is arranged to the left-most position of the range it governs. This move
makes the Japanese postposition phrase have an identical order to the Chinese preposition phrase.

4We further delete several Japanese functional
morphemes which do not have exact Chinesetranslations. They are as follows.

Experiment

We tested our approach on the ASPEC-JC data(Nakazawa et al., 2014). For the source sideJapanese sentences, we used MeCab (IPA dictionary)2 for morpheme analysis, CaboCha3 (Kudoand Matsumoto, 2002) for chunking and dependency parsing. We used the Stanford Chinese
Word Segmenter4 (Tseng et al., 2005) with theChinese Penn Treebank standard (CTB) to seg-

• topic marker wa• nominative case-marker ga• accusative case-marker wo• conjunctive particle te2
http://mecab.googlecode.com/svn/
trunk/mecab/doc/index.html
3
https://code.google.com/p/cabocha/
4
http://www-nlp.stanford.edu/software/segmenter.shtml

We illustrate examples of our pre-reordering approach in Figs. 3 and 4.
79

ment each Chinese sentence. We used the phrasebased (PB) translation system in Moses5 (Koehn etal., 2007) as a baseline SMT system. Word alignment was automatically generated by GIZA++6(Och and Ney, 2003) with the default settingof Moses, and symmetrized by the grow-diagfinal-and heuristics (Koehn et al., 2003). Inphrase extraction, the max-phrase-length was 7with GoodTuring option in scoring. The language model used in decoding is an interpolated
modified Kneser-Ney discounted 5-gram model,
trained on the English side of the training corpus by SRILM7 (Stolcke, 2002). In decoding, thedistortion-limit was 9. The MERT (Och, 2003)
was used to tune the feature weights on the development set and the translation performance wasevaluated on the test set with the tuned weights.
We used identical decoding settings on development and test sets.
Our approach reached a test set BLEU of 28.18
with CTB segmentation in the final evaluation,
which had a slight improvement compared withthe 28.01 of the organizer’s PB SMT baseline.
As to the reordering measure RIBES, our approach reached a score of 0.8087, which had amediocre improvement compared with the organizer’s 0.7926. In the human evaluation, our approach also had an improvement of 6.5 percent according to the organizer’s evaluation score.

5

nese and Japanese is very high even without prereordering, which suggests Japanese has a much
more similar word order with Chinese than English. Then, we conducted more comparisons. We
calculated the average ⌧ of French and English,
which are usually referred as a language pair withsimilar word order, on Europarl V7 data8 (Koehn,2005). As a result, the score is 0.898, which is
not significantly higher than the 0.847 between
Chinese and Japanese in our experiment. We further calculated an average ⌧ between Chinese andJapanese on a parallel corpus with 400, 000 sentences crawled from Internet9 and the score decreased to 0.764, which, however, is still relatively
high. So we conclude that Chinese and Japanese
can be classified as a language pair with fairly similar word order but not as a language pair with significantly different word order.
As to the phenomenon, we consider a main reason is that Chinese is not a typical SVO language.
Chinese can even have a SOV word order with thehelp of a specific particle ba preceding the object.
As a typical topic-prominent language, the word
order in Chinese is affected more by semantic constraints than by syntactic ones. We show an example in Fig. 5 to illustrate the strengths and weaknesses of our approach.
From the above discussion, we conclude thatJapanese-to-Chinese is a relatively easier translation task than Japanese-to-English, and thus astate-of-the-art PB SMT system can achieve an acceptable performance. On the other hand, rather
than the word order problem, which we havefound not such a serious issue, we consider theerrors in word alignment around Japanese functional morphemes is a critical issue in our experiment. Japanese has a sophisticated system ofcase-markers, particles, and auxiliary verbs, which
are deeply tangled with its syntax and semantics. However, these functional morphemes usually have not exactly corresponding translationsin Chinese, and an unsupervised automatic wordaligner tends to scatter the alignment of them. We
assume that using more explicit syntax information in training and decoding phrases of an SMT
system may improve the alignment of these functional morphemes and lead to a better performancein translation quality.

Discussion

In a further investigation, we used the Kendall’s⌧ (Isozaki et al., 2012) on training data as an automatic measure, to investigate the difference inword order between Chinese and Japanese and theeffect of our pre-reordering approach.
We discovered that the baseline Japanese-toChinese word alignment already had an average⌧ of 0.847 and our approach only improved thescore to a slightly higher one of 0.865, which explained why our pre-reordering approach cannot
lead to a significant improvement. In Isozaki et al.
(2012), their pre-reordering approach (head finalization) improves the average ⌧ from 0.43 to 0.75
in English-to-Japanese translation and In Ding etal. (2014), their pre-reordering approach improves
the average ⌧ from 0.49 to 0.67 in Japanese-toEnglish translation. However, the ⌧ between Chi5

http://www.statmt.org/moses/
http://code.google.com/p/giza-pp/7
http://www.speech.sri.com/projects/
srilm/
68980

http://www.statmt.org/europarl/
http://japanese.donga.com/

Chinese

首先介绍了

把Original
Japanese

まず、X

Y

Pre-reorderedJapanese

Chineseまず、首先を紹介した

介绍了

XとY

的

Zを紹介した

した

Yと把作为

X

した作为

YX

的Z

ZZX
YZ

石油依赖的减少和原子能发电的增大
石油依存の低減と原子力発電の増大
全球变暖的防治对策要点
地球温暖化の防止対策骨子
上次预测的概要
前回見通しの概要

Figure 5: Example of pre-reordering for a Japanese sentence and the automatically generated wordalignment of its Chinese translation. The upper row and the lower row are identical Chinese sentence.
The two rows in middle are original and pre-reordered Japanese sentences. X, Y , Z are monotonic nounphrases not affecting the sentence structures. The pre-reordering approach can move the final-positioned
verb properly and lead to a more correct alignment of functional words. However, the original Japanese
sentence has already had a similar word order as the Chinese sentence except the final verb, while thepre-reordering conducted a rigid reordering of swapping X and Y, which leads to an excess move.

6Conclusion and Future Work

Sho Hoshino, Yusuke Miyao, Katsuhito Sudoh, andMasaaki Nagata. 2013. Two-stage pre-ordering forJapanese-to-English statistical machine translation.
In Proc. of IJCNLP, pages 1062–1066.

We propose a rule-based pre-reordering approachfor Japanese-to-Chinese SMT, using the dependency parsing of source-side Japanese sentences.
The approach can bring a mediocre improvementin automatic and human evaluation metrics. We
investigate the experimental results and discoverthat Chinese and Japanese actually have relatively
similar word orders. It seems that the proposed approach is too rigid to handle the word reordering ofJapanese-to-Chinese translation task. We plan toconduct deeper investigation of the two languages
and design more flexible pre-reordering rules.

Hideki Isozaki, Katsuhito Sudoh, Hajime Tsukada, andKevin Duh. 2012. HPSG-based preprocessing forEnglish-to-Japanese translation. ACM Transactions
on Asian Language Information Processing, 11(3).
Article 8.
Jason Katz-Brown and Michael Collins. 2008. Syntactic reordering in preprocessing for Japanese English
translation: MIT system description for NTCIR-7
patent translation task. In Proc. of NTCIR, pages
409–414.
Philipp Koehn, Franz Josef Och, and Daniel Marcu.
2003. Statistical phrase-based translation. In Proc.
of HTL-NAACL, pages 48–54.

Acknowledgment

Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris
Callison-Burch, Marcello Federico, Nicola Bertoldi,Brooke Cowan, Wade Shen, Christine Moran,
Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra
Constantin, and Evan Herbst. 2007. Moses: Open
source toolkit for statistical machine translation. InProc. of ACL, pages 177–180.

We thank Mitsuo Yoshida for providing parallel
data on Japanese and Chinese crawled form Internet, which helps us to investigate the characters of
the two languages.

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