Consistent Improvement in Translation Quality of Chinese–JapaneseTechnical Texts by Adding Additional Quasi-parallel Training Data
Yves Lepage
Graduate School of IPS
Waseda University
2-7 Hibikino, Wakamatsu-ku
Kitakyushu, Fukuoka 808-0135, Japanyves.lepage@waseda.ja

Wei Yang
Graduate School of IPS
Waseda University
2-7 Hibikino, Wakamatsu-ku
Kitakyushu, Fukuoka 808-0135, Japankevinyoogi@akane.waseda.ja
Abstract

experimental results on parallel sentence extraction from quasi–comparable corpora indicated that
their proposed system performs significantly better than previous studies.
There also exist some works on extracting parallel parallel sentences from comparable corpora,such as Wikipedia. Smith et al. (2010) include
features which make use of the additional annotation given by Wikipedia, and features using an
automatically induced lexicon model.
In this paper, we propose to construct a bilingual corpus of quasi-parallel sentences automatically. This is different from parallel or comparable or quasi-comparable corpora. A quasi-parallel
corpus contains aligned sentence pairs that aretranslations to each other to a certain extent. The
method relies on a certain number of existing parallel sentences and a small number of unaligned,
unrelated, monolingual sentences. To construct
the quasi-parallel corpus, analogical associations
captured by analogical clusters are used. The motivation is that the construction of large bilingual
corpora is a problem for less-resourced language
pairs, but it is to be noticed that the monolingual
data are easier to access in large amounts. The languages that we tackle in this paper are: Chineseand Japanese.
Our approach leverages Chinese and Japanesemonolingual data collected from the Web by clustering and grouping these sentences using analogical associations. Our clusters can be considered as
rewriting models for new sentence generation. We
generate new sentences using these rewriting models starting from seed sentences from the monolingual part of the existing parallel corpus we used,
and filter out dubious newly over-generated sentences. Finally, we extract newly generated sentences and assess the strength of translation relations between them based on the similarity, acrosslanguages, between the clusters they were generated from.

Bilingual parallel corpora are an extremely
important resource as they are typicallyused in data-driven machine translation.
There already exist many freely available
corpora for European languages, but almost none between Chinese and Japanese.
The constitution of large bilingual corpora
is a problem for less documented language
pairs. We construct a quasi-parallel corpus
automatically by using analogical associations based on certain number of parallel corpus and a small number of monolingual data. Furthermore, in SMT experiments performed on Chinese-Japanese, byadding this kind of data into the baseline
training corpus, on the same test set, theevaluation scores of the translation results
we obtained were significantly or slightly
improved over the baseline systems.

1

Introduction

Bilingual corpora are an essential resource for current SMT. So as to enlarge such corpora, technology research has been done in extracting parallel
sentences from existing non-parallel corpora. The
approaches and difficulties depend on the parallelness of the given bilingual parallel corpus. Fungand Cheung (2004) give a detailed description of
the types of non-parallel corpora. They proposed
a completely unsupervised method for mining parallel sentences from quasi-comparable bilingual
texts which include both in-topic and off-topic
documents. Chu et al. (2013) proposed a novel
method of classifier training and testing that simulates the real parallel sentence extraction process.
They used linguistic knowledge of Chinese character features. Their approach improved in several
aspects and worked well for extracting parallel
sentences from quasi–comparable corpora. Their
69

Proceedings of the 1st Workshop on Asian Translation (WAT2014), pages 69‒76,
Tokyo, Japan, 4th October 2014.

2

Chinese and Japanese Linguistic
Resources

3

Constructing Analogical Clusters
According to Proportional Analogies

2.1

Chinese and Japanese Parallel Sentences

3.1

Proportional Analogies

Proportional analogies establish a structural relationship between four objects, A, B, C and D: ‘A
is to B as C is to D’. An efficient algorithm for the
resolution of analogical equations between stringsof characters has been proposed in (Lepage, 1998).
The algorithm relies on counting numbers of
occurrences of characters and computing edit distances (with only insertion and deletion as edit operations) between strings of characters (d (A, B) =d (C, D) and d (A,C) = d (B, D)). The algorithm
uses fast bit string operations and distance computation (Allison and Dix, 1986).

The Chinese and Japanese linguistic resources we
use in this paper are the ASPEC-JC1 corpus. It isa parallel corpus consisting of Japanese scientificpapers from the reference database and electronic
journal site J-STAGE of the Japan Science andTechnology Agency (JST) that have been translated to Chinese after receiving permission fromthe necessary academic associations. The parts selected were abstracts and paragraph units from thebody text, as these contain the highest overall vocabulary coverage.
This corpus is designed for Machine Translationand is split as below (some statistics are given in
Table 1):

3.1.1

We gather pairs of sentences that constitute proportional analogies, independently in Chinese andJapanese. For instance, the two following pairsof Japanese sentences are said to form an analogy, because the edit distance between the sentence pair on the left of ‘::’ is the same as between the sentence pair on the right side: d (A, B)= d (C, D) = 13 and d (A,C) = d (B, D) = 5, and therelation on the number of occurrences of characters, which must be valid for each character, may
be illustrated as follows for the character 茶: 1 (inA) - 1 (in B) = 0 (in C) - 0 (in D). We call any such
two pairs of sentences a sentential analogy.

• Training Data: 672,315 sentences;• Development Data: 2,090 sentences;• Development-Test Data: 2,148 sentences;• Test Data: 2,107 sentences.
For new sentence generation from the training data, we extracted 103,629 Chinese-Japaneseparallel sentences with less than 30 characters inlength. We propose to make use of this part ofdata as seed sentences for new sentence generation in both languages, then deduce and constructa Chinese—Japanese quasi-parallel corpus that wewill use as additional data to inflate the baseline
training corpus.
2.2

Sentential Analogies

あなたは
あなたは
ビール
紅茶が
紅茶が好
ビールが
:: が 飲 み :
飲みた :
き で す
好きです
い。
たい。
か。
か。
I’d
like
Do you like :: I’d like a : Do you likea cup of :black tea?
beer?
beer.
black tea.

Chinese and Japanese Monolingual
Sentences

3.1.2

To generate new quasi-parallel data, we also use
unrelated unaligned monolingual data. We collected monolingual Chinese and Japanese short
sentences with less than 30 characters in size fromthe Web using an in-house Web-crawler, mainly
from the following websites: “Yahoo China”, “Yahoo China News”, “douban” for Chinese and “Yahoo! JAPAN”, “Mainichi Japan” for Japanese.
Table 2 gives the statistics of the cleaned 70,000
monolingual data that we used in the experiments.

Analogical Cluster

When several sentential analogies involve thesame pairs of sentences, they form a series of analogous sentences, and they can be written on a sequence of lines where each line contains one sentence pair and where any two pairs of sentencesfrom the sequence of lines forms a sentential analogy. We call such a sequence of lines an analogical cluster. The size of a cluster is the numberof its sentential pairs. The following example inJapanese shows three possible sentential analogies
and the size of the cluster is 3. English translation
is given below.

1 http://orchid.kuee.kyoto-u.ac.jp/
ASPEC/

70

Language
ASPEC-JC
ChineseJapanese# ofdifferent
sentences
668,942666,938

size of sentencesin characters

totalcharacters

total
words

voc. size±±±

31,462,440
39,987,827

18,847,514
23,480,703

295,580
145,074

mean
46.93
59.69

std.dev.
26.62
32.05

Table 1: Statistics on the ASPEC Chinese–Japanese corpus used for training (672,315 sentences). Segmentation tools: urheen for Chinese and mecab for Japanese.

ChineseJapanese# ofdifferent
sentences(cleaned)
70,00070,000size of sentencesin characters(mean ± std.dev.)

total
characters

total
words

±
±

775,530
1,139,588

525,462765,085

10.29
15.06

6.21
6.34

Table 2: Statistics on the cleaned Chinese and Japanese monolingual short sentences. Segmentation
tools: urheen for Chinese and mecab for Japanese.
あなたは紅茶が好きですか。
ビ ー ル が 飲 み た あなたはビールが:好きですか。
い。
ジュースが飲みた あなたはジュース:が好きですか。
い。

3.3

紅茶が飲みたい。 :The steps for determining corresponding clustersare,• First, for each sentence pair in a cluster,
we extract the change between the left andthe right sides by finding the longest common subsequence (LCS) (Wagner and Fischer, 1974).

I’d like a cup of Do you like:black tea?
black tea.
I’d like a beer.
: Do you like beer?
I’d like some juice. : Do you like juice?

• Then, we consider the changes between theleft (Sle f t ) and the right (Sright ) sides in one
cluster as two sets. We perform word segmentation2 on these changes in sets to obtain
minimal sets of changes made up with words
or characters.

As we will see in Section 4, analogical clusters
can be considered as rewriting models. New sentences can be generated using them.
3.2

Experiments on clusters production• Finally, we compute the similarity between
the left sets (Sle f t ) and the right sets (Sright )of Chinese and Japanese clusters. To thisend, we make use of the EDR dictionary3 andword-to-word alignments (based on ASPECJC data using Anymalign4 ), We keep 72,610
word-to-word correspondences obtained with

In each language, independently, we also constructanalogical clusters from the unrelated monolingual data. The number of unique sentences used
is 70,000 for both languages. Table 3 summarizes
some statistics on the clusters produced.

# of different sentences# of clusters

Chinese70,00023,182

Determining corresponding clusters bycomputing similarity

Japanese
70,00021,9752 Segmentation toolkits: Mecab, Part-of-Speech and Morphological Analyzer: http://mecab.googlecode.
com/svn/trunk/mecab/doc/index.html
forJapanese and Urheen, a Chinese lexical analysis toolkit(National Laboratory of Pattern Recognition, China) for
Chinese.
3 http://www2.nict.go.jp/out-promotion/
techtransfer/EDR/index.html
4 http://anymalign.limsi.fr

Table 3: Statistics on the Chinese and Japaneseclusters constructed from our unrelated monolingual data independently in each language.
71

Anymalign in 1 hour after filtering on bothtranslation probabilities with a threshold of
0.3, the quality of these word-to-word correspondences is about 96%. We also use a
traditional-simplified Chinese variant table5
and Kanji-Hanzi Conversion Table6 to translate all Japanese words into Chinese, or convert Japanese characters into simplified Chinese characters. We calculate the similarity
between two Chinese and Japanese word setsaccording to a classical Dice formula:Sim =2 × |Szh ∩ S ja ||Szh | + |S ja |In this example, the solution of the analogicalequation is D = “あなたはビールが好きですか。” (Do you like beer?). If we regard each sentence pair in a cluster as a pair A : B (left to right orright to left), and any short sentence not belonging
to the cluster as C (a seed sentence), the analogical equation A : B :: C : D of unknown D can beforged. Such analogical equations allow us to produce new candidate sentences. Each sentence pairin a cluster is a potential template for the generation of new candidate sentences.

(1)
4.2

Here, Szh and S ja denote the minimal sets ofchanges across the clusters (both on the leftor right) in both languages (after translationand conversion). To compute the similarity
between two Chinese and Japanese clusters
we take the arithmetic mean on both sides, as
given in formula (2):1
SimCzh −C ja = (Simle f t + Simright )2

For the generation of new sentences, we make useof the clusters we obtained from the experiments
in Section 3.2 as rewriting models. The seed sentences as input data for new sentences generation
are the unique Chinese and Japanese short sentences from the 103,629 ASPEC-JC parallel sentences (less than 30 characters). In this experiment, we generated new sentences with each pairof sentences in clusters for Chinese and Japanese
respectively. Table 4 gives the statistics for new
sentence generation.

(2)

We set different thresholds for SimCzh −Cja andcheck the correspondence between these extracted
clusters by sampling. Where the SimCzh −Cja threshold is set to 0.300, the acceptability of the correspondence between the extracted clusters reaches
78%. About 15,710 corresponding clusters wereextracted (SimCzh −Cja ≥ 0.300) by the above steps.

4

Generating New Sentences Using
Analogical Associations

4.1

Generation of New Sentences

Experiments on New Sentences
Generation and Filtering by N-sequences

To filter out invalid and grammatically incorrectsentences and keep only well-formed sentenceswith high fluency of expression and adequacy of
meaning, we eliminate any sentence that contains
an N-sequence of a given length unseen in the reference corpus. This technique to assess the quality of outputs of NLP systems has been used inprevious works (Lin and Hovy, 2003; Doddington,
2002; Lepage and Denoual, 2005). In our experiment, we introduced begin/end markers to makesure that the beginning and the end of a sentence
are also correct. The best quality was obtained forthe values N=6 for Chinese and N=7 for Japanesewith the size of reference corpus (about 1,700,000
monolingual data for both Chinese and Japanese).
Quality assessment was performed by extracting
a sample of 1,000 sentences randomly and checking manually by native speakers. The grammatical quality was at least 96%. This means that
96% of the Chinese and Japanese sentences may
be considered as grammatically correct. For new
valid sentences, we remember their corresponding
seed sentences and the cluster they were generated
from.

Analogy is not only a structural relationship. It isalso a process (Itkonen, 2005) by which, “given
two related forms and only one form, the fourth
missing form is coined” (de Saussure, 1916). If
the objects A, B, C are given, we may obtain another unknown object D according to the analogical equation A : B :: C : D. This principle can beillustrated as follows with sentences:
あなたは紅 ビ ー ル
紅茶が飲: 茶 が 好 き で :: が 飲 み : x
みたい。
すか。
たい。
あなたはビールが好
⇒ x=
きですか。
5 http://www.unicode.org/Public/
UNIDATA/
6 http://www.kishugiken.co.jp/cn/
code10d.html

72Chinese
99,53823,182
105,038,200
Q= 29%
unique seed–new–#33,141
67,099Q= 96%# of seed sentences# of clusters# of candidate sentences

Initial data
New sentence
generation
Quality assessment# of new valid sentences(filtered)Japanese
97,152
21,97580,183,424
Q= 40%
unique seed–new–#40,23484,533
Q= 96%

Table 4: Statistics on new sentence generation in Chinese and Japanese. Q is the quality of the new
candidate sentences or new valid sentences after filtering.
ChineseJapanese

seed–new–#seed–new–#

67,09984,533

Initial parallel
corpus103,629

Chinese–JapaneseCorresponding Quasi-parallel
corpusclusters
15,71035,817

Table 5: Statistics on the quasi-parallel corpus deducing.
4.3

Deducing and Acquiring Quasi-parallel
Sentencestuning set is 2,090 sentences from the ASPECJC.dev corpus, and 2,107 sentences also fromthe ASPEC-JC.test corpus were used for testing.
We perform all experiments using the standard
GIZA++/MOSES pipeline (Och and Ney, 2003).
Adding Additional Quasi-parallel Corpus:
The statistics of the data used in this second setting are given in Table 6 (right). The training corpus is made of 708,132 (672,315 + 35,817) sentences, i.e., the combination of the initial Chinese–
Japanese parallel corpus used in the baseline andthe quasi-parallel corpus.
Experimental Results: Table 7 and Table 8
give the evaluation results. We use the standard
metrics BLEU (Papineni et al., 2002), NIST (Doddington et al., 2000), WER (Nießen et al., 2000),TER (Snover et al., 2006) and RIBES (Isozaki etal., 2010). As Table 7 shows, significant improvement over the baseline is obtained by adding thequasi-parallel generated data based on the Moses
version 1.0, and Table 8 shows a slightly improvement over the baseline is obtained by adding thequasi-parallel generated data based on the Moses
version 2.1.1.

We deduce translation relations based on the initial parallel corpus and corresponding clusters between Chinese and Japanese. If the seeds of two
new generated sentences in Chinese and Japaneseare aligned in the initial parallel corpus, and if theclusters which they were generated from are corresponding, we suppose that these two Chinese andJapanese newly generated sentences are translations of one another to a certain extent. Table 5
gives the statistics on the quasi-parallel deducing
obtained. Among the 35,817 unique Chinese–
Japanese quasi-parallel sentences obtained, about
74% were found to be exact translations by manual
check on a sampling of 1,000 pairs of sentences.
This justifies our use of the term “quasi-parallel”
for this kind of data.

5

SMT Experiments

5.1

Experimental Protocol

To assess the contribution of the generated quasiparallel corpus, we propose to compare two SMT
systems. The first one is constructed using theinitial given ASPEC-JC parallel corpus. This isthe baseline. The second one adds the additional
quasi-parallel corpus obtained using analogical associations and analogical clusters.
Baseline: The statistics of the data used in
the experiments are given in Table 6 (left). The
training corpus consists of 672,315 sentences of
initial Chinese–Japanese parallel corpus. The

5.2

Influence of Segmentation on Translation
Results

We also use Kytea7 to segment Chinese andJapanese. Table 9 and Table 10 show the evaluation results by using Kytea as the segmentation
7 http://www.phontron.com/kytea/
index-ja.html

73その結果，その昼夜ともに環境基準の地点は，平成１MT output
５年度は６３箇箇 所 （３６．
(our base-:２％）では，平成１６年度
line)
は５９箇
箇 所 （３７．８％）であった。
その結果，その昼夜ともに環境基準の地点は，平成１MT output
５年度は６３箇箇 所 （３６．
(our base-:２％）では，平成１６年度
line+add)
は５９箇
箇 所 （３７．８％）であった。
As the Example2 shows, we obtained the betterand more correct translation results based on ourtranslation systems. Correct meaning of a word
or a sentence depends context information. The
large training data in the same domain is also an
extremely important factor in translation systems.
They allow us to obtain the well-formed translation result with high fluency of expression and adequacy of meaning.

tools based on standard GIZA++/MOSES (different version in 1.0 and 2.1.1) pipeline. As the evaluation scores (BLEU and RIBES) shown in Table 7, Table 8, Table 9 and Table 10:• We obtained more increase based on Moses
version 1.0 than Moses version 2.1.1 by using urheen/mecab or kytea for Chinese andJapanese as the segmentation tools;• But, based on Moses version 2.1.1 we obtained higher BLEU and RIBES than Moses
version 1.0 by using two different segmentation tools;• Based on the same Moses version, most of
the BLEU and RIBES scores are higher byusing urheen and mecab as the segmentation
tools for Chinese and Japanese than using
kytea (except ja-zh by using kytea based on
Moses version 2.1.1).
5.3

Issues for Context-aware MachineTranslation

6

Context-aware plays an important role in disambiguation and machine translation. Usually, theMT systems look at surface form only, conversational speech tends to be more concise and more
context-dependent (Example1), and some ambiguities often arises due to polysemy (Example2 fromour experiment results by using urheen and mecab
as the segmentation tools) and homonymy.
Example1: 下次我要尝尝白的。
Reference en: I’ll try Chinese wine next time.
今度は中
中 国 の ワ イ ン を試し
Reference ja:てみます。
MT output en: Next time I’ll try the white.
次回は私は白白 を試してみま
MT output ja:
す。
结果发现，其中昼夜均符合环境标准的地点是，平成１
Example2: ５ 年 度 为 ６ ３ 处 （ ３ ６ ．
２％），平成１６年度为５９处
处（３７．８％）。
その結果 ，全地点のうち昼
夜ともに環境基準地を達成したのは ，平成１５年度のReference ja:
６３地
地点（３６ ．２％ ），平成１６年度で５９地点（３７．８％）であった。
それは、昼と夜が環境基準に沿ったものである場所
MT output: が63（36.2％ ） が 平 成15年
(google)であることが判明した、平成59（37.8％）が16歳。

Conclusion

We presented a technique to automatically generate a quasi-parallel corpus to inflate the training corpus used to build an SMT system. The
experimental data we use are ASPEC-JC corpus
and the monolingual data were collected from theWeb. We produced analogical clusters as rewriting
models to generate new sentences, and filter newly
over-generated sentences by the N-sequences filtering method. The grammatical quality of thevalid new sentences is at least 96%. We thenassess translation relations between newly generated short sentences across both languages, relying on the similarity between the clusters acrosslanguages. We automatically obtained 35,817
Chinese–Japanese sentence pairs, 74% of whichwere found to be exact translations. We call suchsentence pairs a quasi-parallel corpus.
In SMT experiments performed on Chinese–
Japanese, using the standard GIZA++/MOSES
pipeline, by adding our quasi-parallel data, wewere able to inflate the training data in a rewarding way. On the same test set, based on different MOSES versions and segmentation tools, allof translation scores significantly or slightly improved over the baseline systems. It should be
stressed that the data that allowed us to get such
improvement are not so large in quantity and not
so good in quality, but we were able to control bothquantity and quality so as to consistently improve74

translation quality.

Chin-Yew Lin and Eduard Hovy. 2003. Automatic evaluation of summaries using n-gram cooccurrence statistics. In Proceedings of the 2003
Conference of the North American Chapter ofthe Association for Computational Linguistics on
Human Language Technology (HLT-NAACL-2003),
pages 71–78.

Acknowledgments
This work was supported in part by Foreign Joint
Project funds from the Kitakyushu Foundation for
the Advancement of Industry, Science and Technology (FAIS).

Toshiaki Nakazawa, Hideya Mino, Isao Goto, Sadao
Kurohashi, and Eiichiro Sumita. 2014. Overview
of the 1st workshop on asian translation. In Proceedings of the 1st Workshop on Asian Translation(WAT2014).

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75Chinese
672,315
18,847,514
28.12 ± 15.20

test

tune

train

Baseline
sentenceswords
mean ± std.dev.

+ Quasi-parallel
sentenceswords
mean ± std.dev.

Japanese
672,315
23,480,703
35.05 ± 18.88

Both experiments
sentenceswords
mean ± std.dev.
sentenceswords
mean ± std.dev.

Chinese2,090
60,45828.93 ± 15.86
2,107
59,59428.28 ± 14.55

Chinese708,132
19,212,187
27.13 ± 14.19

Japanese
708,132
24,512,079
34.23 ± 17.22

Japanese
2,090
73,17735.01 ± 18.87
2,107
72,02734.18 ± 17.43

Table 6: Statistics on the Chinese–Japanese corpus used for the training, tuning, and test sets in baseline (left) and baseline + quasi-parallel data (right). The tuning and testing sets are the same in bothexperiments. Segmentation tools: urheen for Chinese and Mecab for Japanese.

zh-ja
ja-zh

baseline+ additional training databaseline+ additional training data

BLEU
29.10
32.03
22.98
24.87

NIST
7.56777.97417.01037.3208WER
0.53520.50690.54810.5273TER
0.54780.51720.5711
0.5482RIBES
0.78010.7906
0.78930.8013

Table 7: Evaluation results for Chinese–Japanese translation across two SMT systems (baseline andbaseline + additional quasi-parallel data), Moses version: 1.0, segmentation tools: urheen and mecab.

zh-ja
ja-zh

baseline+ additional training databaseline+ additional training data

BLEU
33.41
33.68
25.53
25.80

NIST
8.15378.18207.38857.4571WER
0.49670.49550.52270.5176TER
0.50610.50390.54270.5378RIBES
0.7956
0.79640.80530.8060

Table 8: Evaluation results for Chinese–Japanese translation across two SMT systems (baseline andbaseline + additional quasi-parallel data), Moses version: 2.1.1, segmentation tools: Urheen and Mecab.

zh-ja
ja-zh

baseline+ additional training databaseline+ additional training data

BLEU
28.35
28.87
22.83
23.18

NIST
7.3123
7.4637
6.95337.0402WER
0.56670.5566
0.56330.5547TER
0.57410.56150.58530.5778RIBES
0.76100.77390.7807
0.7865

Table 9: Evaluation results for Chinese–Japanese translation across two SMT systems (baseline andbaseline + additional quasi-parallel data), Moses version:1.0, segmentation tools: Kytea.

zh-ja
ja-zh

baseline+ additional training databaseline+ additional training data

BLEU
33.27
33.56
26.25
26.52

NIST
7.95798.02297.4931
7.5523WER
0.52490.51780.51970.5128TER
0.52720.5206
0.53980.5335RIBES
0.78200.78490.80850.8105

Table 10: Evaluation results for Chinese–Japanese translation across two SMT systems (baseline andbaseline + additional quasi-parallel data), Moses version: 2.1.1, segmentation tools: Kytea.
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