Source Phrase Segmentation and Translation
for Japanese-English Translation Using Dependency Structure
Junki Matsuo
Kenichi Ohwada
Mamaru Komachi
Graduate School of System
Graduate School of System
Graduate School of System
Design, Tokyo Metropolitan Design, Tokyo Metropolitan Design, Tokyo Metropolitan
University, Japan
University, Japan
University, Japanmatsuoohwadakomachi@tmu.ac.jp
junki@ed.tmu.ac.jp kenichi@ed.tmu.ac.jp

Abstract

SOV of the source language (Japanese) and thencorrectly produce the SVO of the target language(English). Concretely, we devise a dependencybased method that extracts a sentence’s frame(hareafter “basic frame”), consisting of the predicate and its direct children (hereafter “anchor
words”), and its dependent phrases consisting of
the anchor words and their all descendants. After extracting these words and phrases, our method
translates them separately and then combines theirtranslation.
We conducted an experiment with the proposed
method on a Japanese-to-English task at the Second Workshop on Asian Translation (Nakazawa etal., 2015). Although the results of our method are
not positive, we discuss potential improvements.
The rest of this paper is organized as follows.
In the next section, we discuss related work. In
Section 3, the details of our method are explained.
Then, we describe our experiments and analyze
the results.

There are various approaches to statisticalmachine translation (SMT). In particular,
phrase-based SMT (PBSMT) is used as ade facto standard for many language pairs
because it works robustly across languages
and it is easy to implement. However,
the results of PBSMT can include ungrammatical sentences, since it typically does
not take syntactic structure into account.
To overcome this problem, we propose alinguistically motivated approach based on
segmenting a source phrase using a dependency structure and translating eachphrase with PBSMT. This paper presents
the results of our method on JapaneseEnglish translation and discusses potential
improvements.

1

Introduction

It is difficult for statistical machine translation(SMT) to perform translation between languages
such as Japanese and English, which have a systematic difference in their word orders: typically,
Japanese is a subject-object-verb (SOV) language,
whereas English is a subject-object-verb (SVO)
language.
Although PBSMT is used as a de facto standard for many language pairs because it works robustly across languages and it is easy to implement; it typically does not take syntactic structure into account. It is difficult to recognize syntactic information for phrase-based SMT therefore
it cannot handle long-distance reordering that frequently occurs in these language pairs.
To incorporate syntactic information into the
PBSMT framework, we attempt to identify the2 Related WorkA substantial, systematic difference in word orders creates difficulty for SMT, especially, PBSMT, which is not based on syntactic phrases.
Good translation can be achieved in such situations by segmenting the input sentences into portion for simpler and adequate scale inputs.
For translating a long and complex sentencecomposed of several clauses in English into
Japanese translation, Sudoh et al. (2010) proposed segmenting the sentence into clauses that
include non-terminals as placeholders corresponding to embedded clauses using an HPSG parser,
translating the clauses, and then replacing the nonterminals with the corresponding clause’s translations. By representing an embedded clause with
99

Proceedings of the 2nd Workshop on Asian Translation (WAT2015), pages 99‒104,
Kyoto, Japan, 16th October 2015.
2015 Copyright is held by the author(s).

Figure 1 : Illustrative example of our method.
rated into the log-linear model of PBSMT, and thephrase segmenter based on the decoder annotates
the source language phrase boundaries. The annotated data are used to train a new phrase segmentation model, which is then reused by the decoder.
This process is performed iteratively, improving
the phrase segmentation model.
Our approach also involves pre-ordering, oneof the means of coping with the reordering problem. It reorders the word order of a source language sentence in the pre-processing phase tobring the sequence of words closer to the word
order of the target language. Previous work addressing pre-ordering in SOV/SVO language pairs
such as Japanese and English includes Isozaki etal. (2010), Komachi et al. (2006), Katz-Brown
and Collins (2008), Xu et al. (2009), and Hoshino
et al. (2013). These methods use some source language information to reorder the words of source
language words with manual rules: morphologicalanalysis (Katz-Brown and Collins, 2008), dependency analysis (Katz-Brown and Collins, 2008),
and predicate argument structure analysis (Komachi et al., 2006; Hoshino et al., 2013). Our
method also uses dependency analysis for preprocessing, but reordering is not performed. We
use a dependency parser only to extract the basic
frame and dependent phrases.

a non-terminal as a placeholder, they reduced the
problem of reordering a complex sentence to asimple problem of word-level reordering. For
each clause translation, non-terminals are treatedas words and the clause including them is translated using the model trained on a clause-levelaligned corpus that they developed. Their method
uses a high-quality HPSG parser to segment an
English sentence, but such a rich parser is not
publicly available for Japanese. Thus, we opt touse dependency analysis to segment Japanese sentences. In addition, they segmented a sentence
into clauses, whereas we segment a sentence into
linguistically-motivated phrases, and in contrast to
their approach, we do not arrange the corpus to besuitable for segmentation unit.
There are other segmentation methods in machine translation. Roh et al. (2001) proposed amethod that recognizes the range of sub-sentencessuch as a relative and a conjunctive clause using sentence pattern information to overcome theproblem of a syntactic ambiguity in a long sentence. Doi et al. (2003) split an input sentence
into some smaller units to deal with long sentencesin speech translation. Their method does not split
sentences in a pre-processing phase or a parsing
phase. It uses partial translation results and somecriteria that judge the results to determine the best
split positions. Other than that, Lee et al. (2012)
proposed training a phrase segmentation modelusing a PBSMT decoder. The model is incorpo100

Figure 3 : Image of a basic frame, anchor words,
and dependent phrases.
segmenting a sentence. The basic frame and dependent phrases are translated using a decoder. In
the translation of a basic frame, each clause except for the predicate is subject to replacement.
We replace the anchor words in the basic framewith the translations of the corresponding dependent phrases.
3.1 Segmentation and Translation
First, a basic frame is created by extracting thepredicate and its direct children. Then, dependentphrases are created by segmenting the phrases that
depend on the predicate. Second, the translation of
the basic frame and those of the dependent phrases
are obtained.
Finally, the output is created by replacing thetranslations of anchor words in the basic framewith those of the corresponding dependent phrases
except for the translation of the predicate in the basic frame.

Figure 2: Flowchart of our method.

3

Source Phrase Segmentation andTranslation Using Dependency
Structure

Because of its ignorance of syntax, PBSMT may
output ungrammatical sentences. Thus, we propose a source-phrase segmentation and translation
approach to compensate for the lack of linguistically motivated information in the source language. We propose two methods. First method
is “segmentation and translation”, and second
method is “segmentation and translation without
preposition”. “Segmentation and translation” isthe method described in Subsection 3.1, and “segmentation and translation without preposition” isdescribed in Subsection 3.2. In Subsection 3.3, we
describe an additional rule for our method (for asentence that includes a substantive verb).
Figures 1 and 2 show an example and outlineof our method, respectively. Although our method
is similar to that of Sudoh et al. (2010)’s work,
we segment a sentence into its basic frame consisting of a predicate, anchor words and the dependent phrases that are the phrases consisting thesubtrees rooted at the anchor words (Figure 3) by

3.2 Segmentation and Translation without
Preposition
We propose another but similar method that does
not replace the preposition in the translation of thebasic frame with one in the translation of dependent phrases. Because the translation of dependent phrases in our first method uses the language
and translation models optimized for a sentence,
our first method might not be able to use a model
optimized for translating phrases.
For example, the adverbial phrase “詳細に (indetail)” in Figure 1 must be translated as an adverbial phrase “in detail”. Our first method translates
the phrase into “detailed”, because the baseline
decoder assumes a sentence as input. The baseline decoder correctly translates the phrase into “indetail” since it recognizes the verb “計算できる(can be calculated)” next to the phrase. In order101

shownisfluctuationenergy
エネルギー
揺らぎ
を
示した
。

no-seg&transseg&trans
seg&trans (w/o prep)

.

■

RIBES
0.641456
0.628897
0.628463

Table 2: BLEU and RIBES of the baseline andour methods.

■■■

error types
Dependency parsing
Translation of a basic frame
Translation of dependent phrases
Total (Each error may overlap)■

Table 1: Alignment example to which the rule
applies. The English word “is” is aligned to “を”included in the bunsetsu-chunk “揺らぎを”.
Hence, without the rule, “is” is replaced by thetranslation of “揺らぎを” and deleted.

frequency
3
18
46
57

Table 3: Types of errors in the first 100 sentencesof the test-set.

not to replace the correct prepositions, our second
method preserves the preposition coming from thebasic frame.

use JUMAN (version 7.0) for segmentation, andGIZA++ (version 1.0.7) for alignment. We use
Moses (version 2.1.1)’s default configurations:monotone, swap, and discontinuous. The language and translation models of Moses are trainedwith the ASPEC. In translating the basic frame anddependent phrases, we use the same language andtranslation models. MERT is performed on the fulldev-set. We follow the split of dev-set and test-set
provided by the organizer of the workshop.
To preprocess the input sentences, basic frames
and dependent phrases are extracted by a dependency parser CaboCha (version 0.68). 1
We use Moses as a baseline. Moses’s settings
are the same as the above settings. Our method is
evaluated using Bilingual Evaluation Understudy(BLEU) and Rank-based Intuitive Bilingual Evaluation Score (RIBES).

3.3 Additional Rule for Predicate Alignment
We observe that there is a problem in aligning
a Japanese predicate to an English counterpart.
Here, if a word governed by VP in an English sentence is not aligned to one of the words includedin the corresponding predicate bunsetsu-chunk of
the Japanese sentence, the word of the English side
will be deleted by our replacement.
For example, when translating “エネルギー揺らぎを示した (energy fluctuation is shown)” in
Table 1, the predicate of Japanese sentence “示した” is not aligned to the English words “is shown”
but to the word “shown”. In this case, the English
word “is” is not aligned to the word in the Japanesepredicate bunsetsu-chunk “示した”; hence, theword will be deleted by the dependent phrases.
To avoid this unwanted occurrence, we add arule to take effect that if there is a substantive verb
in the translation of a basic frame, the replacement will not be applied to the substantive verb,
since this accident occurs only for the substantive
verb in our observation. As a result of this rule, “isshown” remains in the resulting sentence.
Our method uses this rule for both “segmentation and translation” and “segmentation and translation without preposition”.

4

BLEU
18.32
15.85
15.72

4.2 Experimental Results
Table 2 reports our official evaluation results for
the WAT 2015 and an additional experiment after
the official evaluation campaign. Both BLEU and
RIBES deviated from the baseline.

5 Discussion
In Figure 1, we present an example forwhich our first method fails but our second method succeeds to translate. Our first
method creates “DERS ソフトウェアを用いて”,“「ふげん発電所」の線量率を”, “詳細に”, andExperiments

4.1 Experimental Settings
We use three million parallel sentences fromthe Asian Scientific Paper Excerpt Corpus. We

1
We do not use CaboCha for segmentation but only tocreate dependency phrases.

102

“計算できる” as the dependent phrases. These dependent phrases are translated into “using DERS
software”, “the dose rate ‘Fugen power plant”’,“detailed”, and “calculated”, respectively. Our
first method combines them with a translation of
the basic frame to create output “using DERS software the dose rate ‘Fugen power plant’ can be calculated detailed”, but this is ungrammatical.

perspective, we need to perform translation withdifferent models for a basic frame and for dependent phrases.
In order to perform error analysis, we evaluate our methods quantitatively. Table 3 shows thenumber of errors in using a dependency parser(CaboCha in Table 3), translating a basic frameand dependent phrases (Translation of a basic
frame and Translation of dependent phrases in Table 3). The errors resulting from CaboCha are very
few; there are only three instances out of 100.
The most prominent errors originate in phrasetranslation. We can solve the problem by creating optimized language and translation models fornoun phrases. Because the language and translation models must create a noun phrase when dependent phrase is noun phrase, they must be optimized for noun phrases.
The next most frequent errors are translationsof basic frames. We count this type of error when
the translation of a basic frame is not a sentence.
In contrast to the case of dependent phrases, thetranslation of a basic frame must output a sentencewith a predicate. Alternatively, we can re-rank theoutput to remove ungrammatical translations of abasic frame.

This problem is caused by lack of contextual information in the the dependent phrases. For instance, the reason why the translation of the dependent phrase “詳細に” lacks any preposition isthat the proper preposition corresponding to theparticle “に” cannot be identified only by the information obtained from the input “詳細に”. Our
second method can solve this problem. It does not
replace the preposition following the predicate topreserve the plausible preposition that seems to betranslated properly in the translation of the basic
frame.
However, the BLEU and RIBES scores of oursecond method are lower than those of our first
method. Especially, the BLEU scores of our methods differ more than the RIBES scores. We will
discuss the reason below.
We count the number of the pairs of input andreference that have different voices from the sample data, which comprises 100 sentences that weselected as the top 100 sentences from the testset. Thirty-five percent of the translation outputs
differ in their voice from the corresponding reference, and we suppose that this is the cause of thedegradation in BLEU. For example, suppose a reference is in the active voice and the output of our
methods is in the passive voice. Since BLEU penalizes incorrect translation based on n-gram precision, mis-ordering affect BLEU more than RIBIES.

6 Conclusion
This paper proposed segmentation and translation
methods for Japanese to English translation andpresented its evaluation. We discussed the sample data consisting of the top 100 sentences that
we had selected from the test-set. As a result, wefound that the output has three problems: dependency parsing, translation of a basic frame, andtranslation of dependent phrases. In the future, weplan to optimize the language and the translation
models suitable for phrase translation.

In addition, the proposed methods have problems in the translation and language models. Since
dependent phrases are typically noun phrases,
the baseline decoder trained on parallel sentences
might not produce appropriate translations. Moses
tends to translate a noun phrase into a sentence if
the dependent phrase contains a verb in a relative
clause construction. For example, the dependentphrases “水の運動 (the water movement) の基本である (a basis of) 水素結合ネットワークの変化
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