Consistent Improvement in Translation Quality ofChinese–Japanese Technical Texts by Adding Additional
Quasi-parallel Training Data
Wei Yang and Yves Lepage
Graduate School of Information, Production and Systems
Waseda University
kevinyoogi@akane.waseda.jp ; yves.lepage@waseda.jp

Bilingual parallel corpora are an extremely important resource as they are typically usedin data-driven machine translation. There already exist many freely available corpora for
European languages, but almost none between Chinese and Japanese. The constitutionof large bilingual corpora is a problem for less documented language pairs. We constructa 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, by adding this kind of Chinese–Japanese data into the baseline training corpus, on the same test set, the evaluation scores of the translation results we obtained
were significantly or slightly improved over the baseline systems.

Japanese cluster
left part : right part

Chinese cluster
left part : right part经典 游戏 : 游戏很很不 错‘classic game’ ‘The game is very good.’
喜欢经经典 : 很不 错 喜欢‘I like classic.’ ‘Very good, I like it.’经典啊 : 很不错啊‘Classic!’ ‘Very good!’

クラ シック物語 : この 物語ははと て も い い‘classic narrative’ ‘The narrative is very good.’
クラシック音楽 : この 音楽ははと て も い い‘classic music’ ‘The music is very good.’

SMT experiments• Experimental protocol: To assess the contribution of the generated quasi-parallel corpus, we compare two SMT systems. The first one is constructed using the initial given
ASPEC-JC parallel corpus. This is the baseline. The second one adds the additional
quasi-parallel corpus obtained using analogical associations and analogical clusters.

• Sentential analogy:
早急に対応し 早急に対応し 元に戻して 元 に 戻 し て::::て下さい。て欲しい。
下さい。
欲しい。
• Analogical cluster: We can cluster sentential analogies as a sequence of lines, whereeach line contains one sentence pair and where any two pairs of sentences form asentential analogy.
早急に対応して下さい。: 早急に対応して欲しい。
元に戻して下さい。 : 元に戻して欲しい。
やめて下さい。 : やめて欲しい。
• We produced all possible analogical clusters from Chinese and Japanese unrelated
unaligned monolingual data collected from the Web.

• Experiments on new sentence generation and filtering by N-sequences
We eliminate any sentence that contains an N-sequence of a given length unseen in
our data. For valid sentences, we remember their corresponding seed sentences andthe cluster identifiers they were generated from.
ChineseJapanese# of seed sentences
99,53897,152# of clusters
23,182
21,975# of candidate sentences
105,038,200
80,183,424
Q= 29%
Q= 40%
unique seed–new–# unique seed–new–## of filtered sentences
33,141
67,09940,23484,533
Q= 96%
Q= 96%• Deducing and acquiring quasi-parallel sentences
We deduce translation relations based on the initial parallel corpus and corresponding
clusters between Chinese and Japanese.
ChineseChinese Japanese# of different sentences 70,000
70,000# of clusters
23,182
21,975

Japanese

Chinese–JapaneseInitial par- Corresponding Quasi-parallel
seed–new–# seed–new–#allel corpus clusters
corpus
67,09984,533
103,629
15,71035,817

N Such clusters can be considered as rewriting models that can generate new sentences.

A:B经 典 游戏 : 游戏很很不 错::• Extracting corresponding clusters by computing similarity according to a classical Dice
formula:2 × |Szh ∩ Sja|
1Sim =⇒ SimCzh−Cja = (Simlef t + Simright)|Szh| + |Sja|
2Szh and Sja denote the minimal sets of changes across the clusters (both on the left orright) in both languages (after translation and conversion).

喜欢经经典 : 很不 错 喜欢

::

1
2

Yves Lepage. Solving analogies on words: An algorithm, COLING-ACL’98, Volume I, pp. 728-735, Montréal, Aug. 1998.
Ferdinand de Saussure. Cours de linguistique générale, Payot, Lausanne et Paris, [1ère éd. 1916] edition, 1995.

经典啊 : 很不错啊

Cseed
经典电影
‘classic film’:Xnew−zh电影很不错⇒ ‘The film is very good.’很不错电影‘That’s very good, the film.’

A:
B::
Cseed:
Xnew−jaクラシック物語 : この 物語ははと て も い い
この映画はとてもいい:: クラシック映画 ⇒クラシック音楽 : この 音楽ははと て も い い‘The film is very good.’‘classic film’traintune• Proportional analogy establishes a general relationship between four objects A, B, Cand D: ”A is to B as C is to D”. An efficient algorithm for the resolution of analogicalequations has been proposed in (Lepage, 1998)1.
|A|a − |B|a = |C|a − |D|a, ∀ad (A, B) = d (C, D)
A : B :: C : D ⇒
d (A, C) = d (B, D)• Generation of new sentences
We use analogy as an operation by which, given two related forms (rewriting model) andonly one form, the fourth missing form is coined2. Applied on sentences, this principle
can be illustrated as follows:
早急に対応し早急に対応して下さい。 ::: 正式版に戻して下さい。 : xて欲しい。
⇒ x = 正式版に戻して欲しい。

testBuilding analogical clusters according to proportional analogies

train

Generation of new sentences using analogical associations

Baseline
ChineseJapanesesentences672,315672,315
words
18,847,514
23,480,703
mean ± std.dev. 28.12 ± 15.20 35.05 ± 18.88+ Quasi-parallel
ChineseJapanesesentences708,132
708,132
words
19,212,187
24,512,079
mean ± std.dev. 27.13 ± 14.19 34.23 ± 17.22
Both experiments
ChineseJapanesesentences
2,090
2,090
words
60,45873,177
mean ± std.dev. 28.93 ± 15.86 35.01 ± 18.87
sentences
2,107
2,107
words
59,59472,027
mean ± std.dev. 28.28 ± 14.55 34.18 ± 17.43• Experimental results (using the different segmentation tools and moses version):– segmentation tools: urheen and mecab, moses 1.0: significant.
BLEU NIST WER TER RIBES
baseline
29.10 7.5677 0.5352 0.5478 0.7801
zh-ja+ additional training data 32.03 7.9741 0.5069 0.5172 0.7906
baseline
22.98 7.0103 0.5481 0.5711 0.7893
ja-zh+ additional training data 24.87 7.3208 0.5273 0.5482 0.8013– segmentation tools: urheen and mecab, moses 2.1.1BLEU NIST WER
TER RIBES
baseline
33.41 8.1537 0.4967 0.5061 0.7956
zh-ja+ additional training data 33.68 8.1820 0.4955 0.5039 0.7964
baseline
25.53 7.3885 0.5227 0.5427 0.8053
ja-zh+ additional training data 25.80 7.4571 0.5176 0.5378 0.8060– segmentation tools: kytea, moses 1.0BLEU NIST WER TER RIBES
baseline
28.35 7.3123 0.5667 0.5741 0.7610
zh-ja+ additional training data 28.87 7.4637 0.5566 0.5615 0.7739
baseline
22.83 6.9533 0.5633 0.5853 0.7807
ja-zh+ additional training data 23.18 7.0402 0.5547 0.5778 0.7865