Rule-based Syntactic Preprocessing
for Syntax-based Machine Translation
Yuto Hatakoshi, Graham Neubig, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura
Nara Institute of Science and Technology
Graduate School of Information Science
Takayama, Ikoma, Nara 630-0192, Japan{hatakoshi.yuto.hq8,neubig,ssakti,tomoki,s-nakamura}@is.naist.jp
Abstract

In particular, much attention has been paid topreordering (Xia and McCord, 2004; Collins etal., 2005), a class of preprocessing methods for
PBMT. PBMT has well-known problems with language pairs that have very different word order,
due to the fact that the reordering model has difficulty estimating the probability of long distance
reorderings. Therefore, preordering methods attempt to improve the translation quality of PBMT
by rearranging source language sentences into anorder closer to that of the target language. It’s often the case that preordering methods are basedon rule-based approaches, and these methods haveachieved great success in ameliorating the word
ordering problems faced by PBMT (Collins et al.,2005; Xu et al., 2009; Isozaki et al., 2010b).
One particularly successful example of rulebased syntactic preprocessing is Head Finalization(Isozaki et al., 2010b), a method of syntactic preprocessing for English to Japanese translation thathas significantly improved translation quality ofEnglish-Japanese PBMT using simple rules basedon the syntactic structure of the two languages.
The most central part of the method, as indicatedby its name, is a reordering rule that moves the
English head word to the end of the corresponding
syntactic constituents to match the head-final syntactic structure of Japanese sentences. Head Finalization also contains some additional preprocessing steps such as determiner elimination, particle insertion and singularization to generate a sentence that is closer to Japanese grammatical structure.
In addition to PBMT, there has also recently
been interest in syntax-based SMT (Yamada andKnight, 2001; Liu et al., 2006), which translates
using syntactic information. However, few attempts have been made at syntactic preprocessing
for syntax-based SMT, as the syntactic information given by the parser is already incorporated
directly in the translation model. Notable excep-

Several preprocessing techniques using
syntactic information and linguisticallymotivated rules have been proposed to improve the quality of phrase-based machinetranslation (PBMT) output. On the other
hand, there has been little work on similar
techniques in the context of other translation formalisms such as syntax-based
SMT. In this paper, we examine whether
the sort of rule-based syntactic preprocessing approaches that have proved beneficial
for PBMT can contribute to syntax-based
SMT. Specifically, we tailor a highly successful preprocessing method for EnglishJapanese PBMT to syntax-based SMT,
and find that while the gains achievable aresmaller than those for PBMT, significant
improvements in accuracy can be realized.

1

Introduction

In the widely-studied framework of phrase-based
machine translation (PBMT) (Koehn et al., 2003),
translation probabilities between phrases consisting of multiple words are calculated, and translated phrases are rearranged by the reorderingmodel in the appropriate target language order.
While PBMT provides a light-weight framework
to learn translation models and achieves hightranslation quality in many language pairs, it does
not directly incorporate morphological or syntactic information. Thus, many preprocessing methods for PBMT using these types of information
have been proposed. Methods include preprocessing to obtain accurate word alignments by the division of the prefix of verbs (Nießen and Ney, 2000),
preprocessing to reduce the errors in verb conjugation and noun case agreement (Avramidis andKoehn, 2008), and many others. The effectivenessof the syntactic preprocessing for PBMT has been
supported by these and various related works.

34
Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 34–42,
October 25, 2014, Doha, Qatar. c 2014 Association for Computational Linguistics

tions include methods to perform tree transformations improving correspondence between the sentence structure and word alignment (Burkett andKlein, 2012), methods for binarizing parse trees tomatch word alignments (Zhang et al., 2006), andmethods for adjusting label sets to be more appropriate for syntax-based SMT (Hanneman andLavie, 2011; Tamura et al., 2013). It should be
noted that these methods of syntactic preprocessing for syntax-based SMT are all based on automatically learned rules, and there has been little investigation of the manually-created linguisticallymotivated rules that have proved useful in preprocessing for PBMT.
In this paper, we examine whether rule-based
syntactic preprocessing methods designed for
PBMT can contribute anything to syntax-based
machine translation. Specifically, we examine
whether the reordering and lexical processing of
Head Finalization contributes to the improvementof syntax-based machine translation as it did for
PBMT. Additionally, we examine whether it ispossible to incorporate the intuitions behind theHead Finalization reordering rules as soft constraints by incorporating them as a decoder feature. As a result of our experiments, we demonstrate that rule-based lexical processing can contribute to improvement of translation quality ofsyntax-based machine translation.

Original English
S

VP

NP

NP

NN

VBD

DT

NN

John

hit

a

ball

Reordering

Head Final English
S

VP

NP

NP

NN

DT

NN

VBD

John

a

ball

hit

a

ball

Add Japanese Particles
John
va0

hit
va2

Singularize,
Eliminate Determiners
John

a

ball

va0

hit
va2

Figure 1: Head Finalization

Head Finalization is a syntactic preprocessing
method for English to Japanese PBMT, reducing
grammatical errors through reordering and lexical processing. Isozaki et al. (2010b) have reported that translation quality of English-Japanese
PBMT is significantly improved using a translation model learned by English sentences preprocessed by Head Finalization and Japanese sentences. In fact, this method achieved the highest
results in the large scale NTCIR 2011 evaluation(Sudoh et al., 2011), the first time a statistical machine translation (SMT) surpassed rule-based systems for this very difficult language pair, demonstrating the utility of these simple syntactic transformations from the point of view of PBMT.

Japanese word order, the English sentence is firstparsed using a syntactic parser, and then headwords are moved to the end of the corresponding
syntactic constituents in each non-terminal nodeof the English syntax tree. This helps replicate
the ordering of words in Japanese grammar, wheresyntactic head words come after non-head (dependent) words.
Figure 1 shows an example of the applicationof Head Finalization to an English sentence. The
head node of the English syntax tree is connectedto the parent node by a bold line. When this nodeis the first child node, we move it behind the dependent node in order to convert the English sentence into head final order. In this case, moving
the head node VBD of black node VP to the end of
this node, we can obtain the sentence “John a ballhit” which is in a word order similar to Japanese.

2.1 Reordering

2.2 Lexical Processing

The reordering process of Head Finalization uses
a simple rule based on the features of Japanesegrammar.
To convert English sentence into

In addition to reordering, Head Finalization conducts the following three steps that do not affectword order. These steps do not change the word

2

Head Finalization35

3 Syntax-based Statistical MachineTranslation

ordering, but still result in an improvement oftranslation quality, and it can be assumed that theeffect of this variety of syntactic preprocessing is
not only applicable to PBMT but also other translation methods that do not share PBMT’s problemsof reordering such as syntax-based SMT. The threesteps included are as follows:

Syntax-based SMT is a method for statisticaltranslation using syntactic information of the sentence (Yamada and Knight, 2001; Liu et al., 2006).
By using translation patterns following the structure of linguistic syntax trees, syntax-based translations often makes it possible to achieve more
grammatical translations and reorderings compared with PBMT. In this section, we describe
tree-to-string (T2S) machine translation based onsynchronous tree substitution grammars (STSG)(Graehl et al., 2008), the variety of syntax-based
SMT that we use in our experiments.
T2S captures the syntactic relationship between
two languages by using the syntactic structure ofparsing results of the source sentence. Each translation pattern is expressed as a source sentence
subtree using rules including variables. The following example of a translation pattern include
two noun phrases NP0 and NP1 , which are translated and inserted into the target placeholders X0
and X1 respectively. The decoder generates thetranslated sentence in consideration of the probability of translation pattern itself and translationsof the subtrees of NP0 and NP1 .

1. Pseudo-particle insertion
2. Determiner (“a”, “an”, “the”) elimination
3. Singularization
The motivation for the first step is that in contrast to English, which has relatively rigid word
order and marks grammatical cases of many nounphrases according to their position relative to theverb, Japanese marks the topic, subject, and object
using case marking particles. As Japanese particles are not found in English, Head Finalization
inserts “pseudo-particles” to prevent a mistranslation or lack of particles in the translation process.
In the pseudo-particle insertion process (1), we insert the following three types of pseudo-particles
equivalent to Japanese case markers “wa” (topic),“ga” (subject) or “wo” (object).

S((NP0 ) (VP(VBD hit) (NP1 )))→ X0 wa X1 wo utta

• va0: Subject particle of the main verb

T2S has several advantages over PBMT. First,
because the space of translation candidates is reduced using the source sentence subtree, it is often
possible to generate translations that are more accurate, particularly with regards to long-distance
reordering, as long as the source parse is correct.
Second, the time to generate translation results is
also reduced because the search space is smaller
than PBMT. On the other hand, because T2S generates translation results using the result of automatic parsing, translation quality highly depends
on the accuracy of the parser.

• va1: Subject particle of other verbs• va2: Object particle of any verb
In the example of Figure 1, we insert the topic particle va0 behind of “John”, which is a subject of averb “hit” and object particle va2 at the back ofobject “ball.”Another source of divergence between the two
languages stems from the fact that Japanese does
not contain determiners or makes distinctions between singular and plural by inflection of nouns.
Thus, to generate a sentence that is closer toJapanese, Head Finalization eliminates determiners (2) and singularizes plural nouns (3) in addition to the pseudo-particle insertion.
In Figure 1, we can see that applying thesethree processes to the source English sentence results in the sentence “John va0 (wa) ball va2 (wo)hit” which closely resembles the structure of theJapanese translation “jon wa bo-ru wo utta.”

4 Applying Syntactic Preprocessing toSyntax-based Machine Translation
In this section, we describe our proposed method
to apply Head Finalization to T2S translation.
Specifically, we examine two methods for incorporating the Head Finalization rules into syntaxbased SMT: through applying them as preprocessing step to the trees used in T2S translation, and36

through adding reordering information as a featureof the translation patterns.

Original English
S

S

VP

4.1 Syntactic Preprocessing for T2SNP

We applied the two types of processing shown in
Table 1 as preprocessing for T2S. This is similar
to preprocessing for PBMT with the exception that
preprocessing for PBMT results in a transformed
string, and preprocessing for T2S results in a transformed tree. In the following sections, we elaborate on methods for applying these preprocessing
steps to T2S and some effects expected therefrom.

Lexical Processing

NP

NP

NP

NN

VBD

DT

NN

NN

VA

VBD

NN

VA

John

hit

a

ball

John

va0

hit

ball

va2

Figure 2: A method of applying Lexical Processing
4.1.2 Lexical Processing for T2S
In comparison to reordering, Lexical Processing
may be expected to have a larger effect on T2S,
as it will both have the potential to increase alignment accuracy, and remove the burden of learning
rules to perform simple systematic changes thatcan be written by hand. Figure 2 shows an example of the application of Lexical Processing totransform not strings, but trees.
In the pseudo-particle insertion component,
three pseudo particles “va0,” “va1,” and “va2” (asshown in Section 2.2) are added in the source English syntax tree as terminal nodes with the nonterminal node “VA”. As illustrated in Figure 2, particles are inserted as children at the end of the corresponding NP node. For example, in the figure
the topic particle “va0” is inserted after “John,”
subject of the verb “hit,” and the object particle“va2” is inserted at the end of the NP for “ball,”the object.
In the determiner elimination process, terminal
nodes “a,” “an,” and “the” are eliminated alongwith non-terminal node DT. Determiner “a” andits corresponding non-terminal DT are eliminatedin the Figure 2 example.
Singularization, like in the processing for
PBMT, simply changes plural noun terminals totheir base form.

Table 1: Syntactic preprocessing applied to T2S
Preprocessing
Reordering

VP

Lexical Processing

Description
Reordering based on Japanesetypical head-final grammaticalstructure
Pseudo-particle insertion, determiner elimination, singularization

4.1.1 Reordering for T2S
In the case of PBMT, reordering is used to change
the source sentence word order to be closer tothat of the target, reducing the burden on the relatively weak PBMT reordering models. On theother hand, because translation patterns of T2S
are expressed by using source sentence subtrees,
the effect of reordering problems are relatively
small, and the majority of reordering rules specified by hand can be automatically learned in awell-trained T2S model. Therefore, preordering
is not expected to cause large gains, unlike in thecase of PBMT.
However, it can also be thought that preordering
can still have a positive influence on the translation
model training process, particularly by increasingalignment accuracy. For example, training methods for word alignment such as the IBM or HMM
models (Och and Ney, 2003) are affected by word
order, and word alignment may be improved bymoving word order closer between the two languages. As alignment accuracy plays a importantrole in T2S translation (Neubig and Duh, 2014), it
is reasonable to hypothesize that reordering may
also have a positive effect on T2S. In terms of theactual incorporation with the T2S system, we simply follow the process in Figure 1, but output thereordered tree instead of only the reordered terminal nodes as is done for PBMT.

4.2 Reordering Information as Soft
Constraints
As described in section 4.1.1, T2S work well on
language pairs that have very different word order,
but is sensitive to alignment accuracy. On the other
hand, we know that in most cases Japanese word
order tends to be head final, and thus any rules that
do not obey head final order may be the result of
bad alignments. On the other hand, there are somecases where head final word order is not applicable (such as sentences that contain the determiner37

“no,” or situations where non-literal translations
are necessary) and a hard constraint to obey headfinal word order could be detrimental.
In order to incorporate this intuition, we add
a feature (HF-feature) to translation patterns that
conform to the reordering rules of Head Finalization. This gives the decoder ability to discerntranslation patterns that follow the canonical reordering patterns in English-Japanese translation,and has the potential to improve translation quality
in the T2S translation model.
We use the log-linear approach (Och, 2003) to
add the Head Finalization feature (HF-feature). As
in the standard log-linear model, a source sentence f is translated into a target language sentence e, by searching for the sentence maximizing
the score:ê = arg max wT · h(f , e).
e

VP

Source side oftranslation pattern

VBD

NP

hit

x0:NP

Word alignment
Target side oftranslation pattern

x0

wo

utta

1. Apply Reordering tosource translation pattern

VP

Reorderedtranslation pattern
NP

VBD

x0:NP

hit

Target side oftranslation pattern(1)

where h(f , e) is a feature function vector. w isa weight vector that scales the contribution fromeach feature. Each feature can take any real value
which is useful to improve translation quality, suchas the log of the n-gram language model probability to represent fluency, or lexical/phrase translation probability to capture the word or phrasewise correspondence. Thus, if we can incorporate
the information about reordering expressed by the
Head Finalization reordering rule as a features inthis model, we can learn weights to inform the decoder that it should generally follow this canonicalordering.
Figure 3 shows a procedure of Head Finalization feature (HF-feature) addition. To add the
HF-feature to translation patterns, we examine
the translation rules, along with the alignments
between target and source terminals and nonterminals. First, we apply the Reordering to thesource side of the translation pattern subtree according to the canonical head-final reordering rule.
Second, we examine whether the word order of thereordered translation pattern matches with that of
the target translation pattern for which the wordalignment is non-crossing, indicating that the target string is also in head-final word order. Finally,
we set a binary feature (hHF (f , e) = 1) if the target word order obeys the head final order. Thisfeature is only applied to translation patterns forwhich the number of target side words is greaterthan or equal to two.

x0

wo

2. Add HF-feature
if word alignment is
non-crossing

utta

Figure 3: Procedure of HF-feature addition
Table 2: The details of NTCIR7
Dataset
train
dev
test

Lang

Words

Sentences

En
Ja
En
Ja
En
Ja

99.0M
117M
28.6k
33.5k
44.3k
52.4k

3.08M
3.08M
0.82k
0.82k
1.38k
1.38k

Averagelength
32.13
37.99
34.83
40.77
32.11
37.99

5 Experiment
In our experiment, we examined how much eachof the preprocessing steps (Reordering, Lexical
Processing) contribute to improve the translation
quality of PBMT and T2S. We also examined theimprovement in translation quality of T2S by the
introduction of the Head Finalization feature.
5.1 Experimental Environment
For our English to Japanese translation experiments, we used NTCIR7 PATENT-MT’s Patent
corpus (Fujii et al., 2008). Table 2 shows thedetails of training data (train), development data(dev), and test data (test).
As the PBMT and T2S engines, we used theMoses (Koehn et al., 2007) and Travatar (Neubig,2013) translation toolkits with the default settings.

38

Enju (Miyao and Tsujii, 2002) is used to parse English sentences and KyTea (Neubig et al., 2011) isused as a Japanese tokenizer. We generated wordalignments using GIZA++ (Och and Ney, 2003)
and trained a Kneser-Ney smoothed 5-gram LM
using SRILM (Stolcke et al., 2011). Minimum
Error Rate Training (MERT) (Och, 2003) is used
for tuning to optimize BLEU. MERT is replicatedthree times to provide performance stability on test
set evaluation (Clark et al., 2011).
We used BLEU (Papineni et al., 2002) and
RIBES (Isozaki et al., 2010a) as evaluation measures of translation quality. RIBES is an evaluation method that focuses on word reordering
information, and is known to have high correlation with human judgement for language pairs thathave very different word order such as EnglishJapanese.

Table 5: Optimized weight of HF-feature in eachcondition
HF-feature

Reordering

+
+
+
+

+
+

Word
Processing++

Weight of
HF-feature-0.00707078
0.00524676
0.156724-0.121326

Table 3 shows translation quality for each combination of HF-feature, Reordering, and Lexical
Processing. Scores in boldface indicate no significant difference in comparison with the condition that has highest translation quality using
the bootstrap resampling method (Koehn, 2004)(p < 0.05).
For PBMT, we can see that reordering plays an
extremely important role, with the highest BLEU
and RIBES scores being achieved when using Reordering preprocessing (line 3, 4). Lexical Processing also provided a slight performance gain
for PBMT. When we applied Lexical Processing to
PBMT, BLEU and RIBES scores were improved(line 1 vs 2), although this gain was not significant
when Reordering was performed as well.
Overall T2S without any preprocessing
achieved better translation quality than all conditions of PBMT (line 1 of T2S vs line 1-4 of
PBMT). In addition, BLEU and RIBES score of
T2S were clearly improved by Lexical Processing(line 2, 4, 6, 8 vs line 1, 3, 5, 7), and these scores
are the highest of all conditions. On the other
hand, Reordering and HF-Feature addition had no
positive effect, and actually tended to slightly hurttranslation accuracy.

tributed to improve translation quality of PBMT
slightly.
We also investigated the influencethat each element of Lexical Processing (pseudoparticle insertion, determiner elimination, singularization) had on translation quality, and foundthat the gains were mainly provided by particle
insertion, with little effect from determiner elimination or singularization.
Although Reordering was effective for PBMT,
it did not provide any benefit for T2S. This indicates that T2S can already conduct long distance word reordering relatively correctly, andword alignment quality was not improved as muchas expected by closing the gap in word order between the two languages. This was verified by asubjective evaluation of the data, finding very few
major reordering issues in the sentences translated
by T2S.
On the other hand, Lexical Processing functioned effectively for not only PBMT but also T2S.
When added to the baseline, lexical processing on
its own resulted in a gain of 0.57 BLEU, and 0.99
RIBES points, a significant improvement, withsimilar gains being seen in other settings as well.
Table 4 demonstrates a typical example of the
improvement of the translation result due to Lexical Processing. It can be seen that translation
performance of particles (indicated by underlined
words) was improved. The underlined particle isin the direct object position of the verb that corresponds to “comprises” in English, and thus should
be given the object particle “を wo” as in the reference and the system using Lexical Processing. On
the other hand, in the baseline system the genitive“と to” is generated instead due to misaligned particles being inserted in an incorrect position in thetranslation rules.

5.3 Analysis of Preprocessing

5.4 Analysis of Feature Addition

With regards to PBMT, as previous works on
preordering have already indicated, BLEU and
RIBES scores were significantly improved by Reordering. In addition, Lexical Processing also con-

Our experimental results indicated that translation
quality is not improved by HF-feature addition(line 1-4 vs line 5-8). We conjecture that the reason why HF-feature did not contribute to an im-

5.2 Result

39

Table 3: Translation quality by combination of HF-feature, Reordering, and Lexical Processing. Bold
indicates results that are not statistically significantly different from the best result (39.60 BLEU in line
4 and 79.47 RIBES in line 2).
PBMT
T2S
ID
HF-feature Reordering Lexical Processing BLEU RIBES BLEU RIBES
1
32.11
69.06
38.94
78.48
2
+
33.16
70.19
39.51
79.47
3
+
37.62
77.56
38.44
78.48
4
+
+
37.77
77.71
39.60
79.26
5
+
—
—
38.74
78.33
6
+
+
—
—
39.29
79.23
+
+
—
—
38.48
78.44
7
8
+
+
+
—
—
39.38
79.21
Table 4: Improvement of translation results due to Lexical Processing
SourceReference- Lexical Processing+ Lexical Processing

another connector 96 , which is matable with this cable connector 90 , comprises a plurality of
male contacts 98 aligned in a row in an electrically insulative housing 97 as shown in the figure .
この ケーブル コネクタ ９０ と 嵌合 接続さ れ る 相手 コネクタ ９６ は 、 図示 の よう に
、 絶縁 ハウジング ９７ 内 に雄 コンタクト ９８ を 整列 保持 し て 構成 さ れ る 。
この ケーブル コネクタ ９０ は 相手 コネクタ ９６ は 、 図 に 示 す よう に 、 電気 絶縁性 の ハウジング ９７に 一 列 に 並 ぶ 複数 の 雄型 コンタクト ９８ と から 構成 され ている。
この ケーブル コネクタ ９０ と 相手 コネクタ ９６ は 、 図 に 示 す よう に 、 電気 絶縁性 の ハウジング ９７に 一 列 に 並 ぶ 複数 の 雄型 コンタクト ９８ を 有 し て 構成さ れる。

for T2S.
It should be noted that this paper, while demonstrating that the simple rule-based syntactic processing methods that have been useful for PBMT
can also contribute to T2S in English-Japanesetranslation, more work is required to ensure that
this will generalize to other settings. A next step in
our inquiry is the generalization of these results toother proposed preprocessing techniques and otherlanguage pairs. In addition, we would like to try
two ways described below. First, it is likely that
other tree transformations, for example changing
the internal structure of the tree by moving children to different nodes, would help in cases where
it is common to translate into highly divergent syntactic structures between the source and target languages. Second, we plan to investigate other waysof incorporating the preprocessing rules as a soft
constraints, such as using n-best lists or forests toenode many possible sentence interpretations.

provement in translation quality is that the reordering quality achieved by T2S translation was already sufficiently high, and the initial feature led
to confusion in MERT optimization.
Table 5 shows the optimized weight of the HF
feature in each condition. From this table, we cansee that in two of the conditions positive weights
are learned, and in two of the conditions negative
weights are learned. This indicates that there is no
consistent pattern of learning weights that correspond to our intuition that head-final rules should
receive higher preference.
It is possible that other optimization methods,
or a more sophisticated way of inserting these features into the translation rules could help alleviatethese problems.

6

Conclusion

In this paper, we analyzed the effect of applying
syntactic preprocessing methods to syntax-based
SMT. Additionally, we have adapted reorderingrules as a decoder feature. The results showedthat lexical processing, specifically insertion ofpseudo-particles, contributed to improving translation quality, and it was effective as preprocessing

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