Example-based Machine Translation based on Deeper NLP
Toshiaki Nakazawa, Kun Yu

Daisuke Kawahara

Sadao Kurohashi

Graduate School ofInformation Science and Technology
University of Tokyo

National Institute ofInformation and Communications
Technology

Graduate School ofInformatics
University of Kyoto

nakazawa@kc.t.u-tokyo.ac.jp

dk@nict.go.jp

kuro@i.kyoto-u.ac.jp

Abstract
This paper describes our Kyoto-U system that attended
the IWSLT06 Japanese-English machine translation task.
Example-based machine translation is applied in this system
to integrate our study on both structural NLP and machinetranslation.

1. Introduction
Machine translation has been actively studied recently, andthe major approach is Statistical Machine Translation (SMT).
An alternative to SMT is Example-based machine translation(EBMT)[1]. 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, and make MT system maintenance and improvement much easier compared with Rule-Based MachineTranslation.
On the other hand, EBMT is different from SMT in that
SMT hesitates to exploit rich linguistic resources such as abilingual lexicon and parsers; EBMT does not consider sucha constraint. SMT basically combines words or phrases (relatively small pieces) with high probability [2]; EBMT tries
to use larger translation examples. When EBMT tries to uselarger examples, it can better handle examples which are discontinuous as a word-string, but continuous structurally. Accordingly, though it is not inevitable, EBMT can quite naturally handle syntactic information.
Besides that, the difference in the problem setting between EBMT and SMT is also important. SMT is a natural approach when linguistic resources such as parsers anda bilingual lexicon are not available. On the other hand, incase that such linguistic resources are available, it is also natural to see how accurate MT can be achieved using all theavailable resources.
We chose the latter problem setting to conduct EBMT
research, and here we would like to mention two reasons wechose this setting.
First, we are aiming at the improvement of structural
NLP. We have been conducting research on Japanese morphological analyzers, parsers, and anaphora/omission analy-੤Ꮕ (cross)ὐ 䈪 䇮 (point)⓭ὼ (suddenly)䈅䈱 (that)ゞ 䈏 (car)㘧䈶಴䈚䈩 ᧪䈢 䈱䈪䈜 䇯

the carcameat mefrom the sideat the intersection(rush out)

Figure 1: An example of parallel sentence alignment. (Theroot of a tree is placed at the extreme left and phrases areplaced from top to bottom. Correspondences of underlined
words were detected by a bilingual dictionary.)ses. MT is considered as an application of these fundamental technologies. Amelioration of fundamental NLP technologies naturally improves applications, and applications
give some feedback to fundamental NLP, pointing out the
shortcomings. Needless to say, MT is not the only NLP application, and monolingual NLP applications such as manmachine interfaces and information retrieval can benefit fromthe improvement of fundamental NLP.
The second point is that, in practice, we often encounter
some cases in which the EBMT problem setting is suitable.
That is, if there is no huge bilingual corpus which enables
SMT, but some very similar translation examples are available, it would be nice if automatic translation or translation
assistance can be provided by exploiting the examples. For
example, translation of manuals when translations of the oldversion manuals are available, and patent translation whentranslations of the related patents are available. Or, in thetranslation of an article, the translations to a certain point
can be used effectively as translation memory step by step,
because the same or similar expressions/sentences are often
used in an article. In such cases, an EBMT approach whichtries to find larger translation examples is suitable.
This paper describes our Japanese-English EBMT system, Kyoto-U, challenged to IWSLT06, and reports the evaluation results and discussion.

2. 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 isachieved by the following steps, using a Japanese parser, anEnglish parser, and a bilingual dictionary (see Figure 1).
1. Dependency analysis of Japanese and English sentences.
2. Detection of Word/phrase correspondences.
3. Disambiguation of correspondences.
4. Handling of remaining words.
Among IWSLT06 39,953-sentence training data, somepairs consist of two or more sentences. We utilized the pairs
with the same number of Japanese sentences and English
sentences, and separated them into one-to-one Japanese–
English sentence pairs. As a result, we utilized 43,318 sentence pairs.
We explain these alignment steps in detail.
2.1. Dependency Analysis of Japanese and English Sentences
Japanese sentences are converted into dependency structuresusing a morphological analyzer, JUMAN, and a dependency
analyzer, KNP [3]. 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 travel domain conversations and the accuracy is almost the same withnews article sentences.
Japanese dependency structure consists of nodes whichcorrespond with content words. Function words such as postpositions, affixes, and auxiliary verbs are included in content
words’ nodes.
For English sentences, Charniak’s nlparser is used to convert them into phrase structures [4], and then they are transformed into dependency structures by rules defining headwords for phrases. In the same way as Japanese, each content
word composes a node of English dependency tree.
The “Self-trained parser” introduced in ACL-COLING06[5] is used in our system. Since Charniak’s nlparser wastrained on the penn Treebank, it is not necessarily suitable
for travel domain conversations. In some cases, basic English sentences were wrongly parsed by the parser.
2.2. Pronoun Estimation
In Japanese-English translation, omission of pronouns oftencauses problems. In conversational utterances, Japanese pronouns such as “私 (I)”, “あなた (you)”, “これ (this)” areoften omitted, and this could cause erroneous translations.

There are two cases when pronoun omission causes erroneous translations. One is that a pronoun is omitted in atranslation example and not omitted in an input sentence. In
such cases, there is no correspondence for the English pronoun, and it is merged into the other (usually predicate’s)correspondence. If this merged pronoun is used in the translation, it overlaps with the pronoun from the input. For example, if the translation example “ 胃 (stomach) が 痛いのです (ache) ↔ I ’ve a stomachache” is used to translate “ 私 (I)は胃 (stomach) が 痛いです (ache)”, the translation becomes“I I ’ve a stomachache” naively.
The opposite case also causes erroneous translations.
That is, when a pronoun is in a translation example andis omitted in an input, the ungrammatical English sentence
without pronoun is generated. For example, when “ これ(this) を 日本 (Japan) へ 送って下さい (mail) ↔ will you
mail this to Japan” is used to translate “ 日本 (Japan) へ 送って下さい (mail)”, the translation becomes “will you mail toJapan” by eliminating “これを ↔ this”.
To solve these problems, omitted pronouns are estimatedby the clues of modality and subject case. Then, an extra
node, which represents pronoun omission, is inserted.
2.3. Detection of Word/Phrase Correspondences
Japanese word/phrase to English word/phrase correspondences are detected by two methods.
One is to use a Japanese-English dictionary. We utilized
KENKYUSYA Japanese-English and English-Japanese dictionaries. From these two dictionaries, we got about 300K
entries.
The other method handles transliteration. For possible
person names and geo names suggested by the morphological analyzer and Katakana words (Katakana is a Japanesealphabet usually used for loan words), their possible transliterations are produced and their similarity with words in the
English sentence is calculated based on the edit distance. If
there are similar words exceeding the threshold, they are handled as a correspondence.
For example, the following words can be looked as correspondence by the transliteration module, which can not 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)

The units of correspondences are nodes, and function
words in nodes are included in the correspondences of content words. If the bilingual dictionary and transliteration
module detect a correspondence with two or more content
words, the correspondence of two or more nodes is generated accordingly. In Figure 1, for example, the two Japanese
nodes “交差 (cross)” and “点 (point) で ” corresponds to the
one English node “at the intersection”.
After all the correspondences are found, the inserted
nodes explained in section 2.2 are aligned if there are noaligned pronouns in English side.
2.4. Disambiguation of Correspondences
The method described in the previous section sometimes detects ambiguous correspondences, that is, one-to-many or
many-to-many correspondences. Such ambiguity is resolved
based on harmonious criteria.
Suppose there is a correspondence X with ambiguity, andthere is an unambiguous correspondence Y with the distancen in the Japanese dependency tree and the distance m in the
English dependency tree, we give the score 1/n + 1/m tothe correspondence X, since we can consider that the nearer
Y is to X, the more strongly Y supports X. Here we define
the distance of correspondences as the number of traversing
nodes in a dependency tree. For example, in Figure 1, thedistance between “the car” and “came” is 1, and that between“the car” and “at the intersection” is 2.
Then, we accept the correspondence with the highest sumof its neighbouring correspondences’ score, and reject theothers conflicting with the accepted one. This calculationis repeated until all the ambiguous correspondences are resolved.
2.5. Handling of Remaining Words
The alignment procedure so far found all correspondences inparallel sentences. Then, we merge the remaining nodes into
existing correspondences.
First, the root nodes of the dependency trees are handled
as follows. In the given training data, we suppose all parallel
sentences have an appropriate translation relation. Accordingly, if neither root nodes (of the Japanese dependency tree
and the English dependency tree) are included in any correspondences, the new correspondence of the two root nodes isgenerated. 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 node in the NP is ina correspondence, it is merged into the correspondence. Finally, the remaining nodes are merged into correspondencesof their parent (or ancestor) nodes.
In the case of Figure 1, “あの (that)” is merged into thecorrespondence “ 車 (car) ↔ the car”, since it is within 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”.
We call the correspondences constructed so far as basic
correspondences.
2.6. Translation Example Database
Once we detect basic correspondences in the parallel sentences, all basic correspondences and all combination of adjoining basic correspondences (both in Japanese and English
dependency trees) are registered into the translation example
database.

From the parallel sentences in Figure 1, the three basic
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 from the side” are registered.

3. Translation
In the translation process, first, a Japanese input sentence
is converted into the dependency structure as in the parallel sentence alignment. Then, translation examples for each
sub-trees are retrieved. Finally, the best translation examples
are selected, and their English expressions are combined togenerate the English translation (Figure 2).
3.1. Retrieval of Translation Examples
At first, the root of the input sentence is set as the retrievalroot, and each sub-tree whose root is the retrieval root is retrieved step by step. If there is no translation example fora sub-tree, the retrieval for the current retrieval root stops.
Then, each child node of the current retrieval root is set to
the new retrieval root and its sub-trees are retrieved.
In the case of Figure 2, 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 Japanese node,
the bilingual dictionary is looked up and its translation isused as an translation example. (If there is no entry in the
dictionary we output nothing for the node.)
3.2. Selection of Translation Examples
Then, among the retrieved translation examples, the good
ones are selected to generate the English translation.
The basic idea of example-based machine translation ispreferring to use larger translation example, which considers
larger context and could provide an appropriate translation.
According to this idea, our system 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 the similarity calculated bya thesaurus. The similar outside node is used as a bond tocombine 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 the outside parent
node of the matching Japanese input sub-tree by 0.3 similarity, and one outside child node is also similar to the corresponding input by 0.4, the score of the translation example

Translation Examples੤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(house)(point)ᤨ (when)䇮

traffic

at the intersection

The lightኅ䈮ὐ䈮(enter)my(rush out)

(cross)

౉䉎

came

౉䉎

(enter)ᤨ (when)㕍䉰䉟䊮(signal)

䈪䈚䈢 䇯

ାภ 䈲

when

entering⣕䈓 (put off)⑳ 䈱 (my)was greenwhen

enteringa housethe intersection

mysignatureLM(blue)

traffic(signal)(was)㕍(blue)

䈪䈚䈢 䇯
(was)

Output

The light
was green

My traffic light was green whenentering the intersection.

Figure 2: An example of Japanese-English translation.
becomes 2.7. 1
The set of translation examples just enough for the input
is searched in a greedy way. That is, the best translation example is selected among all the examples first, and then thenext best example is selected for the remaining input nodes,
and this process is repeated.
3.3. Combination of Translation Examples
It is easy to generate an English expression from a translation example, because it contains enough information about
English dependency structure and word order. The problem
is how to combine two or more translation 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: a child bond anda parent bond.
If there is a child node, it is easy to attach the adjoining
example on it. For example, in Figure 2, the translation example “入る (enter) 時 (when)” has a child bond, “ 家 (house)に ”, corresponding to “a house” in the English side. The adjoining example “ 交差点 (で ) ↔ (at) the intersection” is attached on “家に ”, which means “house” is replaced with “theintersection”.
On the other hand, a parent bond tells that the translation
1 We proposed a method of selecting translation examples based on trans-

lation probability [6]. Though we used size- and similarity-based criteria for
IWSLT06 because of time constraints, we are planning to use probabilitybased criteria from now on.

example modifies its head from the front or from behind, butthere is no information about the order with the other children. Currently, we handle it as the first child if it modifies
from the front; as the last child if it modifies from behind.
In Figure 2, “ 私の ↔ my ” has a parent bond, “ サイン ↔
sign” and it tells that “my” should modify its head from thefront. 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, the order of combining two translation examples is controlled byheuristic rules.
3.4. Handling of Numerals
Numerals in Japanese are translated into English in several
ways.
• cardinal : 124 → one hundred twenty four
• ordinal (e.g., day) : 2 日 → second• two-figure (e.g., room number, year) : 124 → one
twenty four
• one-figure (e.g., flight number, phone number) : 124便 → one two four
• non-numeral (e.g., month) : 8 月 → August
At the time of parallel sentence alignment, it is checked
in which type Japanese numerals are translated.

Translation examples of non-numeral type are used only
if the numerals match exactly (“8 月 → August” cannot be
used to translate “7 月”). However, translation examples ofthe other types can be used by generalizing numerals, andthe input numeral is transformed according to the type. For
example, “2 日 → second” can be used to translate “13 日”,
transforming to the ordinal, “thirteenth”.
3.5. Language Model
Even though we are handling omitted pronouns, not aligned
pronouns are still sometimes left in English side. These pronouns are merged into the head node of the sentence, and it
causes the same problem shown in section 2.2.
To solve this problem, the merged pronoun is marked at
the alignment, and two translations with it and without it are
generated and ranked using a language model of English. In
addition, a bond node, which is not used for translation in anormal case, is also used as a translation candidate when thebond node is a pronoun, and the best translation is selectedusing a language model of English.
In the IWSLT06, we used English sentences in 39,953
training data and Cam Toolkit by CMU for a English language model [7].

4. Japanese Flexible Matching
In natural language, many expressions have almost the same
meaning, which brings great difficulty to many natural language processing tasks, including machine translation.
For example, suppose an input sentence “ ホテル に (to)一番 (best) 近い (near) 駅 (station) はど こ ですか (whereis)” is given to an example-based machine translation system.
Even if a very similar translation example “ 旅館 (Japanesestyle hotel) の (to) 最寄り (nearest) の (of) 駅 (station) は ど
こ ですか (where is) ↔ Where’s the nearest station to the hotel?” exists in the translation memory, a simple exact matching method cannot utilize this example for the translation.
To solve these problems, we use flexible matching
method, which can assimilate the expressive divergence. It
has the following two features:
1. Synonym relations and hypernym-hyponym relations
are automatically extracted from an ordinary dictionary.
2. Extracted synonymous expressions are effectively handled by SYNGRAPH data structure, which can packexpressive divergence.
A thesaurus is a knowledge source to provide synonymand hypernym-hyponym relations. However, existing thesauri are not appropriate for flexible matching. One reason
is that the number of words assigned to one unit is often toolarge, and it is difficult to distinguish synonyms in a narrow
sense from similar words. Such distinction is important formachine translation in which translation examples with almost the same meaning should be retrieved. Information retrieval also can utilize such distinction as a degree of matching score. Another problem of existing thesauri is that theyrarely contain relations between a word and a phrase.
To overcome such problems in existing thesauri, we usean ordinary dictionary to extract wide-coverage and precise synonym and hypernym-hyponym expressions. The extracted expressions include not only hypernym-hyponym relations such as 夕食 (dinner) and 食事 (meal), and basic synonym relations such as 旅館 (Japanese-style hotel) and ホテ
ル , but also adverbial synonym such as 一番 (best) and 最も(most) and synonym relations between a word and a phrase
such as 最寄り (nearest) and 一番 近い (best near).
Another point of our method is introducing SYNGRAPH
data structure to this flexible matching. So far, the effectiveness of handling expressive divergence has been only shownfor information retrieval using a thesaurus. However, it isbased on a bag-of-words approach and does not pay attention
to sentence-level synonymy with syntactic structure. However, machine translation requires such precise handling ofsynonymy, and advanced information retrieval and question
answering also need it. To handle sentence-level synonymy
precisely, we have to consider the combination of expressive
divergence, which may cause combinatorial explosion. To
overcome this problem, we assign an ID to each synonymous
expression and then introduce SYNGRAPH data structure topack expressive divergence.
For a sentence, by applying synonymous expressions extracted in the previous section, many paraphrases can be generated in a combinatorial way. The following are some example paraphrases generated from a fairly short seed sentence.
ホテル に 一番 近い 駅 は どこ ですか(hotel to best near station TOP where is)＝ ホテル に 最も 近い 駅 は どこ ですか(hotel to most near station TOP where is)＝ ホテル の 最寄り の駅 は どこ ですか(hotel to nearest of station TOP where is)＝ 旅館 に ichiban 近い 駅 は どこ ですか(hotel to best near station TOP where is)≒ ホテル に近い 駅 は どこ ですか(hotel tonear station TOP where is)≒ ...

To do a flexible matching, we need to recognize the synonymous relations among this expressive divergence. However, the combination of synonymous expressions will cause
combinatorial explosion, which makes both pre-unfolding
and dynamic search are infeasible.
To handle this problem, we introduce SYNGRAPH data
structure which packs all synonymous expressions and cangenerate all the possible paraphrase sentences. Figure 3
shows an example SYNGRAPH which can generate theabove paraphrases.
The basis of SYNGRAPH is the dependency structureof the original sentence (so, in this paper we always employ a robust parser [3]). In the dependency structure, eachࡎ࠹࡞1. 0 ߦ<hotel> ߦ0.99৻⇟(best) 1. 0
<most>0.99<nearest>ㄭ޿(near ) 1. 0
0.99

㚞(station) 1. 0 ߪߤߎ(where) 1. 0 ߢߔ ߆

Figure 3: Example of SYNGRAPH.

Table 1: JP to EN Evaluation results.
Dev 1
Dev 2
Dev 3
Dev 4
Dev 4 ASR
Test
Test ASR

BLEU
0.50870.48810.44680.19210.15900.16550.1418NIST
9.68039.49189.18835.78805.01075.43254.8804

node consists of one content word and zero or more function
words, which is called as a basic node hereafter. If the content word of a basic node belongs to a synonymous group, a
new node with the SYNID is attached to it, and it is calleda SYN node hereafter. For example, in Figure 3, the shaded
nodes are basic nodes and the other nodes are SYN nodes 2 .
Then, if the expression conjoining two or more nodes
corresponds to one synonymous group, a SYN node will be
added there. For example, in Figure 3,  nearest  is sucha SYN node. Furthermore, if one SYN node has a hyper
synonymous group in the synonymy database, the SYN nodewith the hyper SYNID is also added.
In this SYNGRAPH data structure, each node has ascore, NS (Node Score), which reflects how much the expression of the node is shifted from the original expression.

there were variety of problems, such as parsing errors of bothlanguages, excess and deficiency of the 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 for machinetranslation?”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 average and we cannot claim that. Currently, we
can at least dispel the suspicion that parsing might cause sideeffects and lower translation performance.
As we mentioned above, parsing errors are not a principal cause of translation errors, but these are not a few. Oneof the possible countermeasures is to reconsider the learning
process of an English parser. The English parser used here islearned from the penn Treebank, and seems to be vulnerable
to conversational sentences in travel domain.
Furthermore, it is quite possible to improve parsing accuracies of both languages complementarily by taking advantage of the difference of syntactic ambiguities between the
two languages [8]. This approach may not substantially improve the parsing accuracy of the travel domain sentences,because of their short length, but is promising for translating
longer general sentences.
Other main points are as follows:• Punctuation marks are removed and automatic insertion sometimes failed. This causes parsing errors andleads to translation errors.
• Automatic evaluation methods are a little advantageous to SMT [9],[10].
• The soundness of dictionaries heavily affects on theaccuracy 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’ exanples to the input sentence, such as
those that differ perhaps only with respect to whether
or not it contains a negation adverb such as ’not’.

5. Results and Discussion
Our Japanese-English translation system tried two tasks:
manual manuscript translation and ASR output translation(for ASR output we just translated the best path, though).
Our system utilized Japanese and English parsers and a bilingual dictionary, and it was categorized to “Open Data Track”.
Table 1 shows evaluation scores for development set 1 to
4 and the test set.
We examined the translation results and found out that
it was not the case that there was a few major problems, but
2 The reason why we distinguish basic nodes from SYN nodes is to givepriority to exact matching over synonymous matching.

6. Related Work
MSR’s MT system [11] also applied EBMT integrating syntax structure of both source language and target language.
While our method is different from that work in the follwing
two points:• They use “Logical Form(LF)” [12] which abstracts
away language-particular aspects of the sentence pairsuch as function words. On the other hand, we use
full structure of the sentences, and this can handle not
only shorter, easy sentences but longer, complicatedsentences precisely.

• We use Japanese flexible matching method. Thiscan greately help translations of Japanese because
Japanese has quite a variation in expressions. With
Japanese flexible matching, we can find more correspondences in alignment part, and can use more appropriate examples in translation part.

7. Conclusion
As we stated in Introduction, we not only aim at the development of machine translation through some evaluation
measure, but also tackle this task from the comprehensive
viewpoint including the development of structural NLP. The
examination of translation errors revealed several problems,such as parsing, soundness of dictionaries and selection oftranslation 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.

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