Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japan

Kyoto-U: Syntactical EBMT System for NTCIR-7 Patent Translation Task
Toshiaki Nakazawa Sadao Kurohashi
Graduate School of Informatics, Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japannakazawa@nlp.kuee.kyoto-u.ac.jp kuro@i.kyoto-u.ac.jp

2 System Overview

Abstract
This paper describes “Kyoto-U” MT system that
attended the patent translation task at NTCIR-7.
Example-based machine translation is applied in thissystem to integrate our study on both structural NLP
and machine translation. In the alignment step, consistency criteria are applied to solve the alignment
ambiguities and to discard incorrect alignment candidates. In the translation step, translation examples are
combined using “bond” information, which can handle the word ordering without any statistics.
Keywords: EBMT, structural NLP, consistency.

1 Introduction
Machine translation has been actively studied recently, and the major approach is Statistical MachineTranslation (SMT), especially Phrase-based SMT. An
alternative to SMT is Example-based machine translation (EBMT)[10]. The most important common feature between SMT and EBMT is to use a bilingual
corpus, or translation examples, for the translation of
new inputs. Both methods exploit translation knowledge implicitly embedded in translation examples, andmake MT system maintenance and improvement mucheasier compared with Rule-Based Machine Translation.
On the other hand, EBMT is different from SMT inthat SMT hesitates to exploit rich linguistic resources
such as a bilingual lexicon and parsers; EBMT does
not consider such a constraint. SMT basically combines words or phrases (relatively small pieces) withhigh probability [13]; EBMT tries to use larger translation examples. When EBMT tries to use larger examples, it can better handle examples which are discontinuous as a word-string, but continuous structurally.
Accordingly, though it is not inevitable, EBMT canquite naturally handle syntactic information.
This paper describes our EBMT system, “KyotoU”, challenged to NTCIR-7 patent translation task,
and reports the evaluation results and discussion.

Figure 1 shows the overview of our EBMT system
on Japanese-English translation.
Using the example database, new input sentence istranslated. The input sentence is parsed and transformed into dependency structure. For all the arbitrary
sub-trees, available examples are searched. Translation examples are also parsed in both source and target
sides. Of course there are many available examples forone sub-tree, so we give some scores to the examples
and use the highest scored example. Also there aremany types of sub-tree combinations. We search thebest combination by beam-search based on the calculated scores.
In the example, four examples are used. They are
combined and ﬁnally we can get the output dependency tree. We call the outside nodes of the actuallyused nodes as “bond” nodes. The bond nodes of one
example are replaced by the other example, and thus
two examples can be combined. Using the bond information, we don’t need to consider word or phrase orders. Bond information naturally resolve the reordering problem.

3 Alignment of Parallel Sentences
Our system consists of two modules: an alignment
module for parallel sentences and a translation module
retrieving appropriate translation examples and combining them. First, we explain the alignment module.
The alignment of Japanese-English parallel sentences is achieved by the following steps, using a
Japanese parser, an English parser, and a bilingual dictionary.
1. Dependency analysis of Japanese and English
sentences.
2. Detection of Word/phrase correspondences.
3. Disambiguation of correspondences.
4. Handling of remaining words.

― 401 ―We explain these alignment steps in detail.

Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japan

Translation Examplescame੤Ꮕ (cross)ὐ 䈪 䇮(point)at me⓭ὼ (suddenly)

from the side㘧䈶಴䈚䈩 ᧪䈢 䈱䈪䈜 䇯

Input

੤Ꮕ

⑳ 䈱 (my)ାภ 䈲(signal)to remove

was green(house)(point)ᤨ (when)䇮

The light
ኅ䈮

ὐ䈮
౉䉎

traffic

at the intersection(rush out)

(cross)(enter)

my

when

౉䉎

when

(enter)enteringᤨ (when)⣕䈓 (put off)⑳ 䈱 (my)㕍䉰䉟䊮(signal)

䈪䈚䈢 䇯

ାภ 䈲

enteringa housethe intersection

mysignatureLM(blue)

traffic

Output(signal)(was)

The light㕍My traffic light was green whenentering the intersection.

(blue)䈪䈚䈢 䇯

was green(was)Figure 1. An example of Japanese-English translation.

3.1 Dependency Analysis of Japanese andEnglish Sentences

you

᪥ᮏ ࡛
(in Japan)

will have to fileಖ㝤(insurance)

Japanese sentences are converted into dependency
structures using a morphological analyzer, JUMAN[6], and a dependency analyzer, KNP [5]. These tools
can detect Japanese sentence structures with high accuracy: for the news article domain, 99% for segmentation and POS-tagging, and 90% for dependency
analysis. They are robust enough to handle patent sentences and the accuracy is almost the same with news
article sentences.
Japanese dependency structure consists of nodes
which correspond with content words. Function words
such as postpositions, afﬁxes, and auxiliary verbs areincluded in content words’ nodes.
For English sentences, Charniak’s nlparser is used
to convert them into phrase structures [2], and thenthey are transformed into dependency structures byrules deﬁning head words for phrases. In the same
way as Japanese, each content word composes a nodeof English dependency tree.
Figure 2 shows an example of tree structure. The
root of a tree is placed at the extreme left and phrases
are placed from top to bottom.

3.2 Detection of Word/Phrase Correspondences
To detect the corresponding candidates between
Japanese word/phrase and English word/phrase, we

insurance఍♫ ࡟ ᑐࡋ࡚
(to company)ಖ㝤

an claim(insurance)ㄳồ ࡢ

insurance(claim)⏦ࡋ❧࡚ࡀwith the office(instance)ྍ⬟࡛ࡍ ࡼ
(you can)in JapanFigure 2. Example of alignment.

utilize multiple clues: bilingual dictionaries, transliteration, numeral expression matching, and sub-stringalignment method.
Bilingual Dictionaries
By looking up all of the pairs of Japanese words andEnglish words in a dictionary, corresponding candidates are detected deterministically.
Transliteration
For possible person names and place names suggestedby the morphological analyzer and Katakana words(Katakana is a Japanese alphabet usually used for loan

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Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japanwords), their possible transliterations are automatically produced, and the similarities with words in the
English sentence are calculated based on the edit distance. If there are similar word pairs whose edit distance exceeds a threshold, they are handled as a corresponding candidate.
For example, the following words can be considered as correspondences by the transliteration module,
which cannot be handled by the existing bilingual dictionary entries:
新宿 → Shinjuku ↔ Shinjuku (similarity:1.0)
ローズワイン → rosuwain ↔ rose wine (similarity:0.78)
Numeral Expression Matching
By normalizing numeral expressions in each language,
we get correspondence between them. For example, “
二百六十万” in Japanese and “2.6 million” in English
represent the same number “2600000”.
Sub-String Alignment
Some parallel sentences include particular expressions, technical terms, and sometimes they are liberaltranslations. The clues listed above are not sufﬁciently
able to handle such sentences. In other words, not able
to ﬁnd enough correspondences for aligning the parallel sentences with very high accuracy. Therefore, it isalso necessary to use statistical alignment method.
As one of the statistical alignment methods, we
make use of Cromieres’s work [3]. This method cancreate arbitrary sub-string alignments by computing
the co-occurrence counts of any pair of sub-string inparallel corpus. One big advantage of this method liesin unnecessity of morphological analyzer. This is effective for non-segmented languages such as Japaneseor Chinese.
Let us consider a parallel sentence for example:
Source: 参院選での社会党の大敗は必至と言われる。
Target: It is said that the Social Democratic
Party will suffer a major loss at the House ofCouncillors election.
a parallel dictionary ﬁnds only one correspondence “言われる ↔ said that”. With the statistical method,more correspondences, “参院 ↔ the House of Councillors”, “選 ↔ election”, “の社会 ↔ the social”,“党の ↔ Democratic Party” are found.
As a result of using the information above, for example, the correspondence candidates “日本 (Japan)↔ Japan”, “請求 (claim) ↔ claim”, “申し 立て (allegation) ↔ ﬁle / claim”, and combination of “保険(insurance) ↔ insurance” are found (ﬁgure 2).

3.3 Selection of Correspondence Candidates
Results of previous procedure may contain ambiguous or incorrect correspondence candidates.
In ﬁgure 2, for example, Japanese word “保険 (insurance)” and English word “insurance” occurs twice
for each sentence, so there happens ambiguity. Moreover, “申し 立て (allegation)” has two possible translations, “ﬁle” and “claim” in the English sentence. In
addition, unambiguous but incorrect correspondencecandidates might be sometimes detected. Thus, weneed to select plausible correspondences among correspondence candidates.
In order to construct globally consistent alignment,
we deﬁne “consistency score” for a pair of correspondences [12]. We select the best set of correspondencesby the following equation:
n n
i=1
j=i+1 cons.score(ai , aj )argmax(1)n(n − 1)/2
alignment
where ai and aj are one of correspondences.
The detail is explained in the following sections.
3.3.1 Consistency of Alignment
What is most important for the accuracy of alignmentis the way of selecting correct correspondences among
many candidates including ambiguous or unsuitableones. Therefore, robust architecture which can alignthe parallel sentence as a whole is indispensable.
Using only a statistical method, it is difﬁcult to realize high accuracy alignment for a linguistically different language pair such as Japanese-English. In contrast to this, our alignment system is based on dependency tree structure and using deeper NLP, it has thegreat advantage of absorbing the difference of linguistic structure with rich information.
Before introducing our proposed method, let us
think of alignment consistency itself. In ﬁgure 3, thetriangles represent the clauses of tree structure of eachlanguage, and the lines represent the correspondences.
Among all the correspondences, one (on which a crossis placed) seems to be strange, which disturbs the consistency of whole alignment.
This inconsistency is visually apparent. To measure the inconsistency quantitatively, we focus on the“distance” in each language tree structure between twolines. In the example, although the distance between
two lines in source language is far, the distance in target language is near. Since the tree structure is constructed based on dependency information, such a caserarely happens. In other words, it is unlikely that twocorresponding phrase pairs are structurally close in target language and they are far in source language simultaneously.
Therefore, suitably capturing the distance in eachtree structure of all the pairs of lines leads to the overall― 403 ―

Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japan

Japanesepredicate:level C
predicate:level B+/B
predicate:level B-/A
case no / rentai
inside clausepredicate:level Aothers

Near!
Far!

3

EnglishS/SBAR/SQ ...
VP/WHADVPWHADJP
ADVP/ADJPNP/PP/INTJ
QP/PRT/PRN
others5
43

1Figure 4. Deﬁnition of dependency typedistance.

Figure 3. Example of consistency.

consistent alignment of the parallel sentence. For thispurpose, we introduce “consistency score”, which is
explained in the following sections.
3.3.2 Consistency Score
To obtain consistent alignment within a sentence, wedeﬁne a consistency score based on the dependency
tree. Consistency scores are calculated for all the combinations of correspondences. If the relation of a pairis proper, the score is positive, otherwise, the score isnegative.
First, we focus on an arbitrary pair of correspondences ai as (pSi , pT i ) and aj as (pSj , pT j ), wherepSi represents the phrase of ai in the source language
and pT i represents the phrase of ai in the target language. pSj and pT j are deﬁned in the same way.
Then, the dependency distance of the source language dS (ai , aj ) is deﬁned as the distance between
pSi and pSj , and the dependency distance of the target
language dT (ai , aj ) is deﬁned as the distance between
pT i and pT j . Then, the consistency score is deﬁned asfollows:
cons.score(ai , aj ) = f (dS , dT )

6
5
4
2
1

(2)

where dS = dS (ai , aj ) and dT = dT (ai , aj ).
f (dS , dT ) is a function that maps a pair of distanceto the score. The detail of the distances dS , dT and thefunction f are illustrated in the following sections.
The consistency of whole alignment is deﬁned as asum of the consistency scores of all the pairs of correspondences (as shown in Equation 1).
Correct correspondences are supported by theirneighbor correspondences (here, neighbor means the
distance is small in both sides). Such relations produce good scores and contribute to the alignment consistency.
3.3.3 Dependency Type DistanceIn this section, we explain how we calculate the dependency distances dS and dT . In a simple setting, all
the distances of each branch are set to 1. However, it

is natural to use deeper knowledge derived from NLP
resources.
The Japanese dependency analyzer KNP outputs
dependency type information for each phrase, andCharniak’s nlparser also outputs phrase tag information. According to such information, we set dependency type score for each branch to show the strengthof separation between a phrase and its parent phrase.
For example, if the branch represents the segmentation of compound noun, the strength of dependency
is strong (dependency score is small), on the other
hand, if it represents the segmentation of clauses, thestrength is weak (dependency score is high).
Since there are at most around 30 pattens of dependency type, the scores are set by hand subjectively andﬁgure 4 shows part of them. An example of applying
dependency type score is shown in ﬁgure 5, where thelabel placed on each branch represents the dependency
type and the number placed above the label represents
dependency type score.
The distances dS and dT between two correspondences are deﬁned as the sum of dependency typescores from one node to the other in source language
and target language respectively. For example, in ﬁgure 5, the distance between the connected two correspondences in Pair 1 is (dS , dT ) = (1, 1). Also in Pair
2, the distance is (dS , dT ) = (1, 7).
Without tree structure, the difference between thesetwo patterns could be regarded as small (in some cases,
no difference) because the two “insurance” is bothclose to the “claim” from the view point of simple
sequence of words. This illustrates the advantage ofusing tree structure.
3.3.4 Distance-Score Function
The distance-score function f (dS , dT ) maps the distance (dS , dT ) to a score which reﬂects the soundness of the distance. To deﬁne this function, we observed what was the linguistic “behavior” in somereal data. Using a gold standard alignment data [15],which includes about 40K sentence pairs, we learned
the frequency distribution of distance pair. Figure 6
shows the result of automatic learning form gold stan-― 404 ―

Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japan12
108⁘⁤⁗⁣⁧⁗⁠⁕⁫ 6‒‒‒‒‒‒‚⁞⁡⁙‛ 4
2
0

12
10
8
⁘⁤⁗⁣⁧⁗⁠⁕⁫ 6‒‒‒‒‒‒‚⁞⁡⁙‛ 4201412108642⁖⁛⁥⁦⁓⁠⁕⁗‒⁛⁠⁦⁓⁤⁙⁗⁦‒⁥⁛⁖⁗

014
0 0 2 4 6 8 10 122⁖⁛⁥⁦⁓⁠⁕⁗‒⁛⁠⁥⁡⁧⁤⁕⁗‒⁥⁛⁖⁗

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0246⁖⁛⁥⁦⁓⁠⁕⁗‒⁛⁠⁦⁓⁤⁙⁗⁦‒⁥⁛⁖⁗

4 6 8 10 12 14⁖⁛⁥⁦⁓⁠⁕⁗‒⁛⁠⁥⁡⁧⁤⁕⁗‒⁥⁛⁖⁗

Figure 6. Distance pair distribution learned from gold standard data.

≖⇭఍[case “de”](in Japan)

≔
૨ራϋ
≖
ᡲဇ

≖
NP

ଐஜ ↖

ὐ䈪䇮
(insurance)

Pair 1:(Ds, Dt) = (1,1)
Positive Score˟ᅈ ↚ ‫↕ↆݣ‬(to company)≔[inside clause] ૨ራϋ

≕
⇴఍
≖
⇒఍

will have to file̬ᨖ[inside clause][renyou]

̬ᨖ

ဎↆᇌ↕ⅻ

≔
NN
≖
NP

(insurance)ᛪ൭ ↝[case “no”][case “ga”]

੤Ꮕ

you

(claim)Pair 2:(Ds, Dt) = (1,7)
Negative Score(instance)ӧᏡ↖ↈ ↷⓭ὼゞ䈏

an claim≔NN䈅䈱

insurance㘧䈶಴䈚䈩 ᧪䈢 䈱䈪䈜 䇯

the carcameat mefrom the sideat the intersection

insurance≖PPwith the office≖PP

in JapanFigure 7. Example of remaining words
handling.

(you can)Figure 5. Example of dependency typedistance and distance score.

of both dependency type distance and distance-scorefunction parameters is a future work.

dard data. The result shows that the frequency of theequidistant pairs are high and that of non-equivalentpairs are extremely low.
Based on our observation of gold standard data, we
design f (dS , dT ) as follows:• Positive if both dS and dT are small, whichmeans the relation between the two correspondences is appropriate.
• 0 if both dS and dT are large, for the relation is
not so important if they are far from each other.
• Negative if the margin between dT and dS islarge, which means the relation between the twocorrespondences is inappropriate.
For example, in ﬁgure 5, a positive score is given to(dS , dT ) = (1, 1), and a negative score to (dS , dT ) =(1, 7). To meet the requirement above, we deﬁned the
distance-score function on the basis of sigmoid function:0f (dS , dT ) =a
1 − 1+e−t(dS ,dT )

if dS , dT > b(3)otherwise

where the parameters for the function, a, b,t(dS , dT ), are set by hand. Automatic optimization

3.4 Handling of Remaining Words
The alignment procedure so far found all correspondences in parallel sentences. Then, we merge the remaining nodes into existing correspondences.
First, the root nodes of the dependency trees arehandled as follows. In the given training data, we suppose all parallel sentences have an appropriate translation relation. Accordingly, if neither root nodes (ofthe Japanese dependency tree and the English dependency tree) are included in any correspondences, the
new correspondence of the two root nodes is generated. If either root node is remaining, it is merged into
the correspondence of the other root node.
Then, both for Japanese and English remaining
nodes, if it is inside of a base NP and another nodein the NP is in a correspondence, it is merged into
the correspondence. Finally, the remaining nodes are
merged into correspondences of their parent (or ancestor) nodes.
In the case of ﬁgure 2, “あの (that)” is merged into
the correspondence “車 (car) ↔ the car”, since it iswithin an NP. Then, “突然 (suddenly)”, “at me” and“from the side” are merged into their parent correspondence, “飛び出して来たのです (rush out) ↔ came”.

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Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japan

3.5 Translation Example Database
Once we detect basic correspondences in the parallel sentences, all basic correspondences and all combination of adjoining basic correspondences (both inJapanese and English dependency trees) are registeredinto the translation example database.
From the parallel sentences in ﬁgure 7, the threebasic correspondences and their combinations such as“交差点で，突然飛び出して来たのです ↔ came at
me from the side at the intersection” and “突然あの車が飛び出して来たのです ↔ the car came at me fromthe side” are registered.

4 Translation
In the translation process, ﬁrst, a Japanese input
sentence is converted into the dependency structure asin the parallel sentence alignment. Then, translation
examples for each sub-trees are retrieved. Finally, thebest translation examples are selected, and their English expressions are combined to generate the English
translation (ﬁgure 1).

4.1 Retrieval of Translation Examples
At ﬁrst, the root of the input sentence is set as theretrieval root, and each sub-tree whose root is the retrieval root is retrieved step by step. If there is no translation example for a sub-tree, the retrieval for the current retrieval root stops. Then, each child node of thecurrent retrieval root is set to the new retrieval root andits sub-trees are retrieved.
In the case of ﬁgure 1, sub-trees from the root node
“でし た (was)” are retrieved: “でし た (was)”, “青(blue) でした (was)”, “信号 (signal) は でした (was)”,“信号 (signal) は 青 (blue) でした (was)” and so on.
Then, sub-trees from “青 (blue)” and sub-trees from “
信号 (signal) は ” are retrieved step by step.
If no translation example is found for a Japanesenode, the bilingual dictionary is looked up and its
translation is used as an translation example. (If there
is no entry in the dictionary we output nothing for thenode.)

4.2 Selection of Translation Examples
Then, among the retrieved translation examples, thegood ones are selected to generate the English translation.
The basic idea of example-based machine translation is preferring to use larger translation example,
which considers larger context and could provide anappropriate translation. According to this idea, oursystem also selects larger examples.
The selection criterion is based on the size of translation examples (the number of matching nodes with

the input), plus the similarities of the neighboring outside nodes, ranging from 0.0 to 1.0 depending on thesimilarity calculated by a thesaurus. The similar outside node is used as a bond to combine two translation
examples, as explained in the next section.
For example, if the size of a translation example
is two, and the outside parent node is similar to theoutside parent node of the matching Japanese input
sub-tree by 0.3 similarity, and one outside child nodeis also similar to the corresponding input by 0.4, thescore of the translation example becomes 2.7.
The set of translation examples just enough for the
input is searched in a greedy way. That is, the besttranslation example is selected among all the examples
ﬁrst, and then the next best example is selected for theremaining input nodes, and this process is repeated.

4.3 Combination of Translation Examples
It is easy to generate an English expression from atranslation example, because it contains enough information about English dependency structure and word
order. The problem is how to combine two or moretranslation examples.
However, in most cases, the bond node is available
outside of the example, to which the adjoining example is attached. There are two types of bond nodes: achild bond and a parent bond.
If there is a child node, it is easy to attach the adjoining example on it. For example, in ﬁgure 1, the translation example “入る (enter) 時 (when)” has a childbond, “家 (house) に ”, corresponding to “a house” inthe English side. The adjoining example “交差点 (で )↔ (at) the intersection” is attached on “家に ”, whichmeans “house” is replaced with “the intersection”.
On the other hand, a parent bond tells that the translation example modiﬁes its head from the front or frombehind, but there is no information about the orderwith the other children. Currently, we handle it as theﬁrst child if it modiﬁes from the front; as the last child
if it modiﬁes from behind. In ﬁgure 1, “私の ↔ my” has a parent bond, “サイン ↔ sign” and it tells that“my” should modify its head from the front. Then,“my” is put to the ﬁrst child of “the light”, before “trafﬁc”.
It is not often the case, but if there is no bond, we
use translation patterns which are automatically acquired from the training corpus [11].

5 Results and Discussion
Our translation system utilized PSD-1 for training[4]. In addition, Japanese and English parsers and abilingual dictionary were used as external resources.
Table 1 and 2 shows the result of formal trun of
NTCIR-7 patent translation task.

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Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japan

Kyoto-U
Moses

BLEU
21.57
27.14

Table 1. Intrinsic JE Evaluation Result.
Improved BLEU-m300 BLEU-m600 Adequacy
21.98
29.35
35.49
2.85
—
36.02
43.40
2.81

We examined the translation results and found outthat it was not the case that there was a few major problems, but there were variety of problems, such as parsing errors of both languages, excess and deﬁciency ofthe bilingual dictionary, and the inaccurate and inﬂexible use of translation examples.
Now, let us discuss the biggest question: “is the current parsing technology useful and accurate enough formachine translation?” If the translation performance
was signiﬁcantly better than the other systems without parsing, we could answer “YES” to the question.
However, unfortunately our performance is not best in
both BLEU and human evaluation.
There is a very interesting point that our results
are in the middle of the whole systems in view of
BLEU score, they are higher-ranked in view of humanevaluation. Compared to the results of Moses (table 1,
JE direction), although our result is more than 5 points
behind in BLEU score, there is almost no differencein human-evaluation, and ours overcomes Moses in
the measure of Adequacy. From this, we can say twothings:• As is often addressed, BLEU score does not always appropriately reﬂect the goodness of thetranslations, and it is unfavorable for comparing
the translation qualities of the systems which are
not using same techniques.
• Our EBMT system exploits the grobal sentence
structrure information even during alignment(training) step. This might lead to the correctsentence structure of the translations and could
achieve reasonably high Adequacy.
For the result of EJ direction, it was not good inboth BLEU score and human-evaluation. This is because our system was developed with the source language supposed to be Japanese, so it works incorrectly
in a case where the source language is not Japanese. In
Japanese sentence, all the phrases depend on the latterphrase than itself, and the last phrase is always a headof the sentence. Of course this is not same to English.
The handling of the case where a phrase depends onthe former phrase was not appropriate.
The up-to-date BLEU scores with the modiﬁcations
above and other trivial problems are shonw in “Improved” columns. There is small improvement in JE
direction, however in the EJ direction, there is about 1
point improvement. While it seems the result was notchanged drastically in view of BLEU score, we could

Fluency
3.35
3.55

Table 2. Intrinsic EJ Evaluation Result.

Kyoto-U
Moses

BLEU
22.65
30.58

Improved
23.54—

Adequacy
2.42
2.90

Fluency
2.54
3.69

see much improvement by manual check of translations. We can say the quality is very close to theMoses.
As we mentioned above, parsing errors are not aprincipal cause of translation errors, but these are nota few. One of the possible countermeasures is to reconsider the learning process of an English parser. The
English parser used here is learned from the penn Treebank, and seems to be vulnerable to patent sentences.
There often occurs typical expressions, mathematical or chemical formulas and so on in patent corpus.
Given such sentences, the parser will produce unusual
parse trees. We did not adopt any pre-processes to thetraining corpus and input sentences. However, it must
be necessary to handle such typical expressions appropriately.
Furthermore, it is quite possible to improve parsing accuracies of both languages complementarily bytaking advantage of the difference of syntactic ambiguities between the two languages [8]. This approach
may not substantially improve the parsing accuracy ofthe patent sentences, but is promising for translating
longer general sentences.
Other main points are as follows:• Automatic evaluation methods are a little advantageous to SMT [7],[1].
• The soundness of dictionaries heavily affects on
the accuracy of alignment.
• The extension rules of remaining nodes should be
revised.
• The constraint of selecting translation examples
should be more robust. It is currently impossible
to use ’almost equal’ examples to the input sentence, such as those that differ perhaps only withrespect to whether or not it contains a negation
adverb such as ’not’.

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Proceedings of NTCIR-7 Workshop Meeting, December 16–19, 2008, Tokyo, Japan

6 Related Work
MSR’s MT system [14] also applied EBMT integrating syntax structure of both source language andtarget language. While our method is different fromthat work in the follwing point: They use “Logical Form(LF)” [9] which abstracts away languageparticular aspects of the sentence pair such as function
words. On the other hand, we use full structure of thesentences, and this can handle not only shorter, easy
sentences but longer, complicated sentences precisely.

7 Conclusion
As we stated in Introduction, we not only aim at
the development of machine translation through someevaluation measure, but also tackle this task from thecomprehensive viewpoint including the developmentof structural NLP. The examination of translation errors revealed several problems, such as parsing, soundness of dictionaries and selection of translation examples. Resolving such problems is considered to be an
important issue not only for MT but also for other NLP
applications. We pursue the study of machine translation from this standpoint continuously.

References[1] C. Callison-Burch, M. Osborne, and P. Koehn. Reevaluating the role of bleu in machine translation research. In Proceedings of 11 th Conference of the European Chapter of the Association for Computational
Linguistics, pages 249–256, 2006.
[2] E. Charniak and M. Johnson. Coarse-to-ﬁne n-best
parsing and maxent discriminative reranking. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pages
173–180, 2005.
[3] F. Cromieres. Sub-sentential alignment using substring co-occurrence counts. In Proceedings of the44th Annual Meeting of the Association for Computational Linguistics, pages 13–18, 2006.
[4] A. Fujii, M. Utiyama, M. Yamamoto, and T. Utsuro.
Overview of the patent trajnslation task at the ntcir-7
workshop. In Proceedings of the 7th NTCIR Workshop
Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answeringand Cross-lingual Information Access, 2008.
[5] S. Kurohashi and M. Nagao. A syntactic analysis
method of long Japanese sentences based on the detection of conjunctive structures. Computational Linguistics, 20(4):507–534, 1994.
[6] S. Kurohashi, T. Nakamura, Y. Matsumoto, and
M. Nagao. Improvements of Japanese morphologicalanalyzer JUMAN. In Proceedings of The International
Workshop on Sharable Natural Language, pages 22–
28, 1994.
[7] G. Leusch, N. Uefﬁng, and H. Ney. Cder: Efﬁcient mt
evaluation using block movements. In Proceedings of

11 th Conference of the European Chapter of the Association for Computational Linguistics, pages 241–248,
2006.
[8] Y. Matsumoto, H. Ishimoto, and T. Utsuro. Structural
matching of parallel texts. In Proceedings of the 31st
Annual Meeting of the Association for Computational
Linguistics, pages 23–30, 1993.
[9] A. Menezes and S. D. Richardson. A best-ﬁrst alignment algorithm for automatic extraction of transfer
mappings from bilingual corpora. In Proceedingsof the 39th Annual Meeting of the Association for
Computational Linguistics (ACL) Workshop on DataDriven Machine Translation, pages 39–46, 2001.
[10] M. Nagao. A framework of a mechanical translation
between Japanese and English by analogy principle.
In Proceedings of the international NATO symposium
on Artiﬁcial and human intelligence, pages 173–180,
1984.
[11] T. Nakazawa and S. Kurohashi. Example-based machine translation using automatically acquired translation patterns for function words. In Proceedings ofthe 14th Annual Meeting of the Natural Language Processing, 2008. in Japanese.
[12] T. Nakazawa, K. Yu, and S. Kurohashi. Structuralphrase alignment based on consistency criteria. In In
Proceedings of Machine Translation Summit XI (MTSummit XI), pages 337–344, 2007.
[13] F. J. Och, C. Tillmann, and H. Ney. Improved alignment models for statistical machine translation. In
Joint SIGDAT Conference on Empirical Methods in
Natural Language Processing and Very Large Corpora, pages 20–28, 1999.
[14] S. D. Richardson, W. B. Dolan, A. Menezes, and
M. Corston-Oliver. Overcoming the customization
bottleneck using example-based mt. In Proceedingsof the workshop on Data-driven methods in machinetranslation - Volume 14, pages 1–8, 2001.
[15] K. Uchimoto, Y. Zhang, K. Sudo, M. Murata,
S. Sekine, and H. Isahara. Multilingual Aligned Parallel Treebank Corpus Reﬂecting Contextual Information and Its Applications. In Proceedings of the
MLR2004: PostCOLING Workshop on Multilingual
Linguistic Resources, pages 63–70, 2004.

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