A Dependency-to-String Model for Chinese-Japanese SMT System
Hua ShanYujie Zhang
Lu BaiTe Luo
School of Computer and Information technology， Beijing Jiaotong University{13120422, yjzhang, 13120379, 14120472}@bjtu.edu.cn

Abstract2011) in Chinese-Japanese translation task.
This method proposes a novel tree decompose
tion, which takes head-dependents relation(HDR) fragments as elementary structures ofrule extraction. An HDR is a tree fragmentcomposed of a head and all its dependents. In
this method, the translation rules are expressedwith the source side as generalized HDR fragments and the target sides as strings. The model takes substitution as the only operation andcan specify reordering information directly intotranslation rules, thus requires no additional
heuristics or reordering models as the previousworks. And the model is more concise.
Section 2 describes dependency-to-stringtranslation model in detail. Section 3 reports on
our experiment results on a Chinese- SMT system. Section 4 concludes this paper.

This paper describes the Beijing Jiaotong University Chinese-Japanesemachine translation system which participated in the 2st Workshop on AsianTranslation (WAT2015). We exploit
the syntactic and semantic knowledge
encoded in dependency tree to build adependency-to-string translation model for Chinese-Japanese statisticalmachine translation (SMT). Our system achieves a BLEU of 34.87 and a
RIBES of 79.25 on the Chinese-Japanese translation task in theofficial evaluation.

1

Introduction

Motivated by representing the grammatical
function of the constituents of a sentence orphrase，dependency grammar holds both syntactic and semantic knowledge．How to building
translation model by exploiting the syntactic andsemantic knowledge encoded in dependency tree
has been now one of the most popular research
topics in the recent years．
In dependency tree based models, researchers propose some tree decomposition methods orgrammars to build translation model. Thesemodels can be classified into string-to-tree model, tree-to-tree model and tree-to-string model.
Our system participated in WAT2015 (Nakazawa et al., 2015) adopts tree-to-string model.
Particularly, we use the dependency-to-stringtranslation method proposed by (Xie et al.,2

Dependency-to-String
ModelTranslation

In this paper, we describes the translation modelin four aspects, dependency-to-string grammar,translation rule acquisition, the model and thedecoding.

2.1 Dependency-to-String Grammar
A dependency structure for a sentence is a directed acyclic graph with words as nodes andmodification relations as edges, each edge directing from a head to a dependent. Figure 1 (a)shows an example dependency structure of a
Chinese sentence.
2010年 FIFA 世界杯 在 南非 成功 举行
82

Proceedings of the 2nd Workshop on Asian Translation (WAT2015), pages 82‒86,
Kyoto, Japan, 16th October 2015.
2015 Copyright is held by the author(s).

2010 FIFA World Cup in South Africa successfully hold
Here are some properties of a HDR fragment :1) head determines the syntactic categoryof HDR, and can often replace HDR;
2) head determines the semantic categoryof HDR; dependent gives semanticspecification.
According to the above properties, we canrepresent the corresponding HDR fragment withhead. The translation rules of dependency-to-string model can be classified into two
categories:-HDR rules, which represent the sourceside as generalized HDR fragments and the target sides as strings and act as both translation
rules and reordering rules.
-H rules, which represent the source side asa word and the target side as words or strings
and are used for translating words.
Figure 1 shows examples of the two translation rules. (b), (c) and (d) are three examplesof HDR rules, and (d) is an example of H rules.
In the figure, the nodes modified by “*” are headof HDR fragment. By the way, the three HDR
rules describes translation ways of the samesentence pattern (that is, constituted by “nounphrase + preposition phrase + adverb + verb” )and different contexts. Thereinto, rule (b) appoints its context completely, rule (c) restrains
its context partially and rule (d) has no restraint
for its context.

(a)

(b)

(c)

(d)

(e)

Figure 1: Examples of dependency structure(a), HDR rules (b), (c) , (d)and H rules (e).
ency trees for translation rule acquisition.
2) Acceptable HDR fragments identification: identify HDR fragments from theannotated trees for HDR rules generation.
3) HDR rules generation: generate a series of HDR rules according to theidentified acceptable HDR fragments.
The following describes each of these indetail.

2.2 Rule Acquisition
The rule acquisition of dependency-to-stringmodel begins with a parallel corpus withword-aligned results, the source dependency
structures and the target side sentence. We accomplish the rule automatic acquisition throughthe following three steps:
1) Tree annotation: annotate the necessary
information on each node of depend

2.2.1

83

Tree Annotation and Acceptable HDR
Fragments Identification4)
5)

The tree annotation can be accomplished bya single postorder transversal of dependency tree
T. For each node n of T, we annotated with headspan hsp(n) and dependency span dsp(n) (Xie etal., 2011). During the recursive walk, we calculate hsp(n) according to alignment relation foreach node n accessed. The dsp(n) can be obtained according to hsp(n) and dependency spanof all dependents of n. After tree annotation, we
can identify HDR fragments for HDR rules generation, according to head span and dependency
span of each node.

2.2.2

2.4 Decoding
Our decoder is based on bottom up chart parsing algorithm that convert the input dependency structure into a target string. It finds the bestderivation among all possible derivations D.
Given a source dependency structure T, thedecoder traverses each internal node n of T in
post-order. And we process it as follows.
1) If n is a leaf node, it checks the rule
set for matched translation rules H
and uses the rules to generate candidate translation;2) If n is a internal node, it enumerates
all instances of the related sentence,clauses or phrases of the HDR fragment rooted at n, and checks thetranslation rule set for matched translation rules. If there is no matchedrules, we construct a pseudo translation rule according to the word orderof the HDR fragment in the sourceside;3) Make use of Cube Pruning algorithm(Chiang, 2007; Huang and Chiang,2007) to generate the candidate translation for the node n.
To balance the decoder’s performance andspeed, we use four constraints as follows:
1) Beam-threshold: we get the scorethreshold from the best score in thecurrent stack multiplied by a fixed ratio. The candidate translations with ascore worse than the score threshold
will be discarded;2) beam-limit: the maximum number ofcandidate translations in the beam;3) rule-threshold: we get the rule scorethreshold from the best score multiplied by a fixed ratio in the rule tablequeue. The rules with a score worse
than rule score threshold will be dis-HDR Rules and H Rules Generation

According to the identified acceptable HDR
fragments, a series of lexicalized and unlexicalized HDR rules will be generated. This paper
will not describe in detail about it and you canrefer to (Xie et al., 2011).
H rules acquisition can be implemented as a
sub procedure of HDR rules acquisition. Specifically, in the recursive walk of dependency tree,a H rule is generated according to alignment information for each node accessed.

2.3 Translation Model
Given the dependency-to-string grammar, for agiven source language dependency tree T, it
may generate more than one derivations D thatconvert a source dependency tree T into a target string e, thus producing varieties of candidate translations. To compare the candidatetranslations, we adopt a general log-linear
model (Och and Ney, 2002) to define D as:P(D) ∝ ∏ ϕi (D) λi

target word penalty: exp(|e|);language model : Plm(e);（1）

where ϕi (D) is feature function defined onderivation D and λi are the feature weights.
Our paper used seven features as follows:
1) translation probabilities: P(t|s) andP(s|t);2) lexical translation probabilities: Plex(t|s) and Plex (s|t);3) rule penalty: exp(-1);
84carded;4) rule-limit: the maximum number ofrules in the rule table queue.
For our experiments, we set thebeam-threshold = 10-2, beam-limit = 100,
rule-threshold = 10-2 and rule-limit = 100.

3SystemRule #BLEU

RIBESBaseline

35M

34.25

78.94

Dep2str

8.8M

34.87

79.25

Table 1 The comparison results of the two systems
Then, we use dependency-to-string model
described in Section 2 to build a Chinese-Japanese translation system. And use theBLUE score and RIBES score for evaluation.

Experiments

3.1 Data preparation
We use ASPEC1 Chinese-Japanese paper excerpt corpus. The training data contains
672,315 sentence pairs, the development datacontains 2090 sentence pairs, and the test datacontains 2107 sentence pairs.
We employ the Stanford Word Segmenter2for Chinese word segmentation, with thestandard of CTB. And we use JUMAN3 forJapanese word segmentation.
After word segmentation, we use theStanford Parser 4 for Chinese dependency
parsing. The parser can create dependency tree
for a Chinese sentence and provide thepart-of-speech (POS) for each node and thedependency relation type for each edge.
Meanwhile, the sentences with special symbols
and the sentences whose dependency results
contain cross phenomenon will be filtered out.
As a result, about 590,000 sentence pairs wereobtained for training data.
We apply SRI Language Modeling
Toolkit5 to train a 4-gram language model on
the Japanese corpus preprocessed.
We obtain the word alignments by running
GIZA++6 on the corpus in both directions andapplying “grow-diag-and” refinement.
We make use of MERT to tune the feature
weights in order to maximize the system’s
BLEU7 score on the development set.

3.2 Experiments and Evaluation ResultsThe Chinese-Japanese translation system(Dep2str) consists of three modules:
1) Rule extraction module: extract rules
using the Chinese dependency tree, theJapanese sentence and alignment information of the training corpus.
2) Decoding module: decode the Chinese sentences for the n-best Japanesetranslations according to the model parameters that have been set.
3) Training module: train the translation
model using minimum error rate to get thebest parameters on the development data.
We then decode the test data using thesystem.
Table 1 shows the number of the extracted
translation rules and the translation performance on the test data. Furthermore, we implemented a MOSES PBSMT system (Koehnet al., 2002) as the baseline for a comparison.
In our experiments the value of the distortion
limit of the baseline system is the default.
The number of translation rules and translation
performance of the baseline system are also
showed in the table.
In terms of the number of translation rules,
the number of the extracted translation rules inthe baseline system is over 3 times more thanthat of dep2str system. We think that the lackof restrictions on syntactic structure resulted inthis. In terms of translation performance, theBLEU score and RIBES score on the test data1

http://orchid.kuee.kyoto-u.ac.jp/ASPEC/http://nlp.stanford.edu/software/segmenter.shtml
3 http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?JUMAN
4 http://nlp.stanford.edu/software/lex-parser.shtml
5 http://www.speech.sri.com/projects/srilm/
6 http://www.statmt.org/moses/giza/GIZA++.html
7ftp://jaguar.ncsl.nist.gov/mt/resources/mteval-v11b.pl
285

achieved by dep2str system are higher than thebaseline system by 0.62 and 0.31 respectively.
These evaluation results illustrate that thetranslation system based on the dependency-to-string model is effective on the Chinese-Japanese translation task.

4of the 2nd Workshop on Asian Translation(WAT2015), October 2015, Kyoto, Japan.
Franz Josef Och and Hermann Ney. 2002. Discriminative training and maximum entropy models forstatistical machine translation. In Proceedings of40th Annual Meeting of the Association for Computational Linguistics, pages 295–302, Philadelph-Conclusions

ia, Pennsylvania, USA, July.

This paper describes the Beijing Jiaotong University Chinese-Japanese machine translation
system participated in WAT2015. The system
employs a dependency-to-string model, whichtakes the HDR fragments as elementary structures for the rule extraction and directly specifies the ordering information in translation
rules, making the decoding algorithm simplified. The experiment results on the ASPEC
data showed that the BLEU score and theRIBES score are increased by 0.62 and 0.31
respectively, compared with the phrase-basedsystem.
At present, the accuracy of the Chinese dependency parsing is not very high, and our system’s performance is affected by the accuracy.
Meanwhile, we filtered out the sentences which
could not be parsed by the dependency parser.
This caused a decrease in the amount of training
data by about 100 thousand sentence pairs. We
think that the system’s performance will be improved with Chinese dependency parsing withhigh accuracy.

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, AlexandraConstantin, and Evan Herbst. 2007. Moses:
Open source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of
the Association for Computational Linguistics
Companion Volume Proceedings of the Demo andPoster Sessions, pages 177-180.
David Chiang. 2005. A hierarchical phrase-basedmodel for statistical machine translation. In Proceedings of 225 ACL 2005, pages 263–270.
Liang Huang and David Chiang. 2007. Forest
rescoring: Faster decoding with integrated language models. In Proceedings of ACL 2007, pages
144–151, Prague, Czech Republic, June.
Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Kat suhito Sudoh, and Hajime Tsukada. 2010. Automaticevaluation of translation quality for distant language pairs. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language

References

Processing, pages 944-952.

Jun Xie，Haitao Mi，and Qun Liu．A novel dependency-to-string model for statistical machinetranslation．In Proceedings of the 201 1 Conference
On Empirical Methods in Natumf Language Processing，pages 216—226，Edinburgh，Scotland，UK·，July 2011·Association for Computational
Linguistics．
Nakazawa, Toshiaki and Mino, Hideya and Goto,
Isao and Neubig, Graham and Kurohashi, Sadaoand Sumita, Eiichiro. 2015. Overview of the 2ndWorkshop on Asian Translation. In proceedings
86