A Dependency-to-String Model for Chinese-Japanese SMT System
Hua Shan Lu Bai Te Luo Yujie Zhang
School of Computer and Information Technology, Beijing Jiaotong University
No.3 Shangyuancun, Haidian District, Beijing, China, 100044{13120422, 13120397, 14120472 , yjzhang} @bjtu.edu.cn

Introduction● Our system employs a dependency-to-string translation method for Chinese-Japanese SMT.
➢ Motivated by that dependency grammar holds both syntactic and semantic knowledge.
● Our system achieves a BLEU of 34.87 and a RIBES of 79.25 on the Chinese-Japanese translation task in the official evaluation.
➢ Our dependency-to-string model improved the BLEU score by 0.62 and the RIBES score by 0.31 on the test set.
➢ The evaluation results illustrated that the translation system based on the dependency-to-string model is effective.

Dependency-to-String Grammar

Decoding
Our decoder is based on bottom up chart parsing algorithm thatconvert the input dependency structure into a target string. It
finds the best derivation among all possible derivations D. Givena source dependency structure T, the decoder traverses eachinternal node n of T in post-order. And we process it as follows.
1) If n is leaf node, it checks the translation rules H and uses thematched rules to generate candidate translation.
2) If n is a internal node, it enumerates all instances of theclauses or phrases of the HDR fragment rooted at n, andchecks the translation rules for matched translation rules. IfThe translation rules of the dependency-to-string model
also act as reordering rules, which are classified into twotypes:-HDR rules, the source side is generalized HDR fragments
and the target sides is strings.
eg. (x1:NR)(x2:P)(x3:AD)举⾏行 ! x1 held x3 x2-H rules, the source side is a word and the target side iswords or strings.
eg. (x1:世界杯)(x2:在)(x3:AD)举⾏行 ! x1 held x3 x2

Rule Acquisition

We implemented the rule acquisition as follows:
1) Tree annotation: annotate the necessary information on eachnode of dependency trees for translation rule acquisition.
2) Identification of acceptable HDR fragments: identify HDR
fragments from the annotated trees for HDR rules generation.
3) HDR rules generation: generate a set of HDR rules according
to the identified acceptable HDR fragments

there is no matched rules, wo construct a pseudo translation ruleaccording to the word order of the HDR fragment in the sourceside.
3) Make use of Cube Pruning algorithm to generate the candidate
translation for the node n.

Our system used seven features as follows:
1) translation probabilities: P(t|s) and P(s|t)2) lexical translation probabilities: Plex (t|s) and Plex (s|t)3) rule penalty: exp(-1)4) target word penalty: exp(|e|)5) language model: Plm(s)Experiment and Evaluation
We used Stanford Word Segmenter for Chinese word segmentation,with the standard of CTB, and used Stanford Parser for Chinese
dependency parsing. We used JUMAN for Japanese word
segmentation and used SRI Language Modeling Toolkit for training a4-gram language model on the Japanese corpus preprocessed. We
obtained the word alignments by running GIZA++ on the corpus inboth directions and applying “grow-diag-and” refinement. We makeuse of MERT to tune the feature weights in order to maximize thesystem’s BLEU score on the development set.
SystemRule #BLEU

RIBESBaseline

35M

34.25

78.94

Ours

8.8M

34.87

79.25

The Table shows the number of the extracted translation rules and thetranslation performance on the test data. Furthermore, weimplemented a MOSES PBSMT system as the baseline forcomparison. Ours performed better than Baseline by using only asmall size of translation rules that is about one fourth of that ofBaseline.