Chinese-to-Japanese Patent Machine Translation based on Syntactic
Pre-ordering for WAT 2015
Katsuhito Sudoh
Masaaki Nagata
NTT Communication Science Laboratories, Japansudoh.katsuhito@lab.ntt.co.jp

Abstract

ChinesesentenceThis paper presents our Chinese-toJapanese patent machine translation
system for WAT 2015 (Group ID: ntt)that uses syntactic pre-ordering over
Chinese dependency structures. A head
word and its modifier words are reorderedby hand-written rules or a learning-to-rank
model. Our system outperforms baseline
phrase-based machine translations andcompetes with baseline tree-to-stringmachine translations.

Chinese word segmentationand part-of-speech tagging

Word/POS
modelsChinese language
resource (CTB)

Chinese dependency parsing

Dep.
modelsChinese language
resource (patent)Pre-orderingrules

Dependency-based
syntactic pre-ordeirng

Pre-orderingmodel

Phrase-based statistical MT

MTmodelsSuppliedparallel text

Japanesesentence

1 Introduction

Figure 1: Brief workflow of our Chinese-toJapanese MT system. Gray resources are in-house
ones.

Patent documents, which well-structured written
documents that describe the technical details of inventions, are expected to have almost no semantic ambiguities caused by indirect or rhetorical expressions. Due to this aspect, patent documents
are good candidates for literal translation, whichmost machine translation (MT) approaches aim todo.
One technical challenge for patent machinetranslation is the complex syntactic structure ofpatent documents, which typically have long sentences that complicate MT reordering, especiallyfor the word order in distant languages. Chinese and Japanese have similar word order innoun modifiers but different subject-verb-object
order, requiring long distance reordering in translation. In this year’s WAT evaluation campaign(Nakazawa et al., 2015), we tackle long distance
reordering by syntactic pre-ordering based on Chinese dependency structures (Sudoh et al., 2014) ina Chinese-to-Japanese patent translation task.
Our system basically consists of three components: Chinese syntactic analysis (word segmentation, part-of-speech (POS) tagging, anddependency parsing) adapted to patent documents; dependency-based syntactic pre-orderingwith hand-written rules or a learning-to-rank
model; and a standard phrase-based statistical MT.
This paper describes our system’s details and discusses our evaluation results.

2 System Overview
Figure 1 shows a brief workflow of our Chineseto-Japanese MT system. Its basic architecture isstandard with syntactic pre-ordering. Input sentences are first applied to word segmentation andPOS tagging, parsed into dependency trees, reordered using pre-ordering rules or a pre-orderingmodel, and finally translated into Japanese by aphrase-based statistical MT.

3 Chinese Syntactic Analysis: WordSegmentation, Part-of-Speech Tagging,and Dependency Parsing
Word segmentation and POS tagging are solved
jointly (Suzuki et al., 2012) for better Chinese
word segmentation based on the POS tag sequences. The dependency parser produces un95

Proceedings of the 2nd Workshop on Asian Translation (WAT2015), pages 95‒98,
Kyoto, Japan, 16th October 2015.
2015 Copyright is held by the author(s).

typed dependency trees. The Chinese analysis
models were trained using in-house Chinese treebanks in the patent domain (about 35,000 sentences) as well as the standard Penn Chinese Treebank dataset (Sudoh et al., 2014). The trainingalso utilized unlabeled Chinese patent documents(about 100 G bytes) for semi-supervised training(Suzuki et al., 2009; Sudoh et al., 2014).

4

dependency trees and have to consider all the possible permutation over one head word and one ormore modifier words.

5 Evaluation
5.1 Setup
We trained a word n-gram language model andtwo different phrase-based translation models by
the above different pre-ordering approaches. We
used all of the supplied Chinese-Japanese bilingual training corpora of one million sentence pairs(except for long sentences over 64 words) for theMT models: phrase tables, lexicalized reordering tables, and word 5-gram language models using standard Moses and KenLM training parameters. We applied modified Kneser-Ney phrase table smoothing with an additional phrase scoring
option: --KneserNey. The model weights wereoptimized by standard Minimum Error Rate Training (MERT), but we compared five independent
MERT runs and chose the best weights for the development test set. The distortion limits were alsochosen from 0, 3, 6, and 9 by comparing the results of the MERT runs. We chose 9 both for therule-based and data-driven pre-ordering.
The pre-ordering model for the data-driven
method was trained by the word alignment used
for the phrase table by a Ranking SVM implementation with Liblinear1 . Its soft margin parameter C
was chosen by the ranking accuracy on the development set.

Syntactic Pre-ordering

We compared two different syntactic pre-ordering
approaches, rule-based and data-driven, in thisevaluation campaign.
4.1 Rule-based Pre-ordering
Rule-based pre-ordering is an intuitive approachthat generates target language word order basedon linguistic knowledge and analysis. For example, in an English-to-Japanese patent MT, a very
simple pre-ordering rule called Head Finalization(Isozaki et al., 2012) gives very successful results.
Our pre-ordering rules are based on Head Final Chinese (Han et al., 2012) developed for Chinese HPSG trees. We modified the HPSG-oriented
rules for dependency structure, but their basic actions are almost the same. One expected advantage of rule-based pre-ordering is that its stability
is independent on bilingual corpora, while modelbased approaches basically depend on bilingualcorpora to determine which reordering is the best.
4.2 Data-driven Pre-ordering by
Learning-to-Rank

5.2 Official Results
Table 1 shows the official evaluation results by theorganizers in Pairwise Cloudsourcing Evaluation
scores (Human), RIBES, and BLEU. Our rulebased system showed slightly better performancein RIBES and BLEU than the tree-to-string baseline, but the difference may not be significant. The
performances of our systems were lower than thetree-to-string baseline in the Human evaluation.
With respect to the difference in the pre-ordering
approaches, the rule-based system outperformed
the data-driven one.

Data-driven pre-ordering obtains the most probable reordering of a source language sentence thatis monotone with the target language counterpart.
It learns rules or models using reordering oracles
over word-aligned bilingual corpora.
We used a learning-to-rank approach with
Ranking SVMs (Yang et al., 2012), which reorders the head word and its modifier words in adependency tree based on their ranks. The features resemble those by Yang et al. (2012); we
did not use label-related ones because our dependency trees do not have labels. The reordering
oracles were determined to maximize Kendall’s τover automatic word alignment in a similar manner to Hoshino et al. (2015). The only differenceis the tree structure; Hoshino et al. (2015) used binary trees and just considered monotone or reverse
for two child nodes of each tree node. But we use

5.3 Discussion
One critical concern is the difference between
tree-to-string baseline and our pre-ordering systems. Syntactic pre-ordering based on child ranking/classification is a simple approximation of a196

http://www.csie.ntu.edu.tw/˜cjlin/libsvmtools/

work will investigate sophisticated pre-ordering
methodology such as pre-ordering lattices orforest-based pre-ordering and better word alignment for data-driven pre-ordering.

Table 1: Official evaluation results in Pairwise Cloudsourcing Evaluation scores (Human),RIBES, and BLEU. RIBES and BLEU are basedon MeCab Japanese word segmentation. Scores inbold are the best ones.
SystemHuman RIBES BLEU
Organizer PBMT
n/a0.781 0.382
Organizer T2S
20.75
0.814 0.394
Ours rule-based
16.25
0.822 0.406
Ours data-driven
8.00
0.812 0.399

Acknowledgments
We thank Dr. Jun Suzuki for his great help on theChinese dependency parsing. The basis of our preordering rules was developed in cooperation withDr. Dan Han, whom we thank. Finally, we greatly
appreciate the workshop organizers for this valuable evaluation campaign.

standard tree-to-string MT2 . The tree-to-string MT
can compare different reordering hypotheses; ourpre-ordering just chooses one-best pre-ordering.
A comparison between the results by rule-based
and data-driven pre-ordering systems suggests our
pre-ordering rules work robustly. Even though weexpected model-based pre-ordering to capture thecomplex reordering phenomena in the dependency
structure, it gave worse results than the rule-based
one. One possible reason is the noisy automaticword alignment in the bilingual corpora; using better word alignment (manual annotation or supervised word alignment) is promising to learn a consistent pre-ordering model (Hoshino et al., 2015).

References
Dan Han, Katsuhito Sudoh, Xianchao Wu, Kevin Duh,Hajime Tsukada, and Masaaki Nagata. 2012. HeadFinalization Reordering for Chinese-to-Japanese
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2
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