Simultaneous English-Japanese Spoken Language Translation
Based on Incremental Dependency Parsing and Transfer
Koichiro Ryu
Shigeki Matsubara
Yasuyoshi Inagaki
Graduate School ofInformation Technology Center,
Faculty ofInformation Science,
Nagoya University
Information Science
Nagoya University
Furo-cho, Chikusa-ku,
and Technology,
Furo-cho, Chikusa-ku,
Nagoya, 464-8601, Japan
Aichi Prefectural University
Nagoya, 464-8601, Japan
Nagakute-cho, Aichi-gun,ryu@el.itc.nagoya-u.ac.jp
Aichi-ken, 480-1198, Japan

Abstract
This paper proposes a method for incrementally translating English spoken language into Japanese. To realize simultaneous translation between languages withdifferent word order, such as English andJapanese, our method utilizes the featurethat the word order of a target language
is flexible. To resolve the problem ofgenerating a grammatically incorrect sentence, our method uses dependency structures and Japanese dependency constraints
to determine the word order of a translation. Moreover, by considering the factthat the inversion of predicate expressions
occurs more frequently in Japanese spoken language, our method takes advantage of a predicate inversion to resolve theproblem that Japanese has the predicate at
the end of a sentence. Furthermore, ourmethod includes the function of cancelingan inversion by restating a predicate when
the translation is incomprehensible due tothe inversion. We implement a prototypetranslation system and conduct an experiment with all 578 sentences in the ATIS
corpus. The results indicate improvements
in comparison to two other methods.

1

Introduction

Recently, speech-to-speech translation has become one of the important research topics inmachine translation. Projects concerning speechtranslation such as TC-STAR (Hoge, 2002) and
DARPA Babylon have been executed, and conferences on spoken language translation such as
IWSLT have been held. Though some speechtranslation systems have been developed so far(Frederking et al., 2002; Isotani et al., 2003; Liuet al., 2003; Takezawa et al., 1998), these systems,
because of their sentence-by-sentence translation,cannot start to translate a sentence until it has been
fully uttered. The following problems may arise incross-language communication:• The conversation time become long since ittakes much time to translate• The listener has to wait for the translation
since such systems increase the difference between the beginning time of the speaker’s utterance and the beginning time of its translation
These problems are likely to cause some awkwardness in conversations. One effective methodof improving these problems is that a translation
system begins to translate the words without waiting for the end of the speaker’s utterance, much asa simultaneous interpreter does. This has been verified as possible by a study on comparing simultaneous interpretation with consecutive interpretation from the viewpoint of efficiency and smoothness of cross-language conversations (Ohara et al.,2003).
There has also been some research on simultaneous machine interpretation with the aim of developing environments that support multilingual
communication (Mima et al., 1998; Casacuberta
et al., 2002; Matsubara and Inagaki, 1997).
To realize simultaneous translation between
languages with different word order, such as English and Japanese, our method utilizes the featurethat the word order of a target language is flexible. To resolve the problem that translation systems generates grammatically dubious sentence,our method utilizes dependency structures andJapanese dependency constraints to determine theword order of a translation. Moreover, by considering the fact that the inversion of predicate expressions occurs more frequently in Japanese spoken language, our method employs predicate inversion to resolve the problem that Japanese has
the predicate at the end of the sentence. Furthermore, our method features the function of canceling an inversion by restating a predicate when thetranslation is incomprehensible due to the inversion. In the research described in this paper, we
implement a prototype translation system, and toevaluate it, we conduct an experiment with all 578
sentences in the ATIS corpus.
This paper is organized as follows: Section
2 discusses an important problem in EnglishJapanese simultaneous translation and explains theidea of utilizing flexible word order. Section 3 introduces our method for the generation in EnglishJapanese simultaneous translation, and Section 4
describes the configuration of our system. Section
5 reports the experimental results, and the paper
concludes in Section 6.

2

Japanese in Simultaneous
English-Japanese Translation

In this section, we describe the problem of thedifference of word order between English andJapanese in incremental English-Japanese translation. In addition, we outline an approach of simultaneous machine translation utilizing linguistic phenomena, flexible word order, and inversion,
characterizing Japanese speech.
2.1 Difference of Word Order between
English and Japanese
Let us consider the following English:(E1) I want to fly from San Francisco to Denver
next Monday.
The standard Japanese for (E1) is(J1) raishu-no (‘next’) getsuyobi-ni (‘Monday’)
San Francisco-kara (‘from’) Denver-he (‘to’)
tobi-tai-to omoi-masu (‘want to fly’).
Figure 1 shows the output timing when the translation is generated as incrementally as possible
in consideration of the word alignments between(E1) and (J1). In Fig. 1, the flow of time is shown
from top to bottom. In this study, we assume
that the system translates input words chunk-bychunk. We define a simple noun phrase (e.g. San

Input

I
want to fly
from
San Francisco
toDenvernext Monday

Outputraishu-no (‘next’) getsuyobi-ni (‘Monday’)
San Francisco-kara (‘from’)Denver-he (‘to’)
tobi-tai-to omoi-masu (‘want to fly’)Figure 1: The output timing of the translation (J1)
Input

Iwant to fly
from
San Francisco
toDenvernext Monday

OutputSan Francisco-kara (‘from’)Denver-he (‘to’) tobi-tai-to omoi-masu (‘want to fly’)raishu-no (‘next’) getsuyobi-ni (‘Monday’)Figure 2: The output timing of the translation (J2)Francisco, Denver and next Monday), a predicate(e.g. want to fly) and each other word (e.g. I, from,to) as a chunk. There is “raishu-no getsuyobi-ni”(‘next Monday’) at the beginning of the translation (J1), and there is “next Monday” corresponding to “raishu-no getsuyobi-ni” at the end of thesentence (E1). Thus, the system cannot output“raishu-no getsuyobi-ni” and its following translation until the whole sentence is uttered. This isa fatal flaw in incremental English-Japanese translation because there exists an essential difference
between English and Japanese in the word order. It
is fundamentally impossible to cancel these problems as long as we assume (J1) to be the translation of (E1).
2.2 Utilizing Flexible Word Order inJapaneseJapanese is a language with a relatively flexible
word order. Thus, it is possible that a Japanesetranslation can be accepted even if it keeps theword order of an English sentence. Let us consider the following Japanese:(J2) San Francisco-kara (‘from’) Denver-he (‘to’)
tobi-tai-to omoi-masu (‘want to fly’) raishu-no(‘next’) getsuyobi-ni (‘Monday’).
(J2) can be accepted as the translation of the sentence (E1) and still keep the word order as close as
possible to the sentence (E1). Figure 2 shows theoutput timing when the translation is generated asincrementally as possible in consideration of theword alignments between (E1) and (J2). The figure demonstrates that a translation system mightbe able to output “San Francisco -kara (‘from’)”
when “San Francisco” is input and “Denver-he(‘to’) tobi-tai-to omoi-masu (‘want to fly’)” when
“Denver” is input. If a translation system outputs the sentence (J2) as the translation of thesentence (E1), the system can translate it incrementally. The translation (J2) is not necessarily
an ideal translation because its word order differsfrom that of the standard translation and it has aninverted sentence structure. However the translation (J2) can be easily understood due to the highflexibility of word order in Japanese. Moreover, in
spoken language machine translation, the high degree of incrementality is preferred to that of quality. Therefore, our study positively utilizes flexible word order and inversion to realize incremental English-Japanese translation while keeping thetranslation quality acceptable.

3

Japanese Generation based on
Dependency Structure

When an English-Japanese translation system incrementally translates an input sentence by utilizing flexible word order and inversion, it is possible that the system will generate a grammatically incorrect Japanese sentence. Therefore, itis necessary for the system to generate the translation while maintaining the translation quality at
an acceptable level as a correct Japanese sentence.
In this section, we describe how to generate an
English-Japanese translation that retains the word
order of the input sentence as much as possible
while keeping the quality acceptable.
3.1 Dependency Grammar in English andJapanese
Dependency grammar illustrates the syntactic
structure of a sentence by linking individual
words. In each link, modifiers (dependents) areconnected to the word that they modify (head). In
Japanese, the dependency structure is usually defined in terms of the relation between phrasal units
called bunsetsu1 . The Japanese dependency relations are satisfied with the following constraints(Kurohashi and Nagao, 1997):• No dependency is directed from right to left.
• Dependencies do not cross each other.
1
A bunsetsu is one of the linguistic units in Japanese, androughly corresponds to a basic phrase in English. A bunsetsu
consists of one independent word and more than zero ancillary words. A dependency is a modification relation between
two bunsetsus.

Dependency relation

Dependent

Headbunsetsu

bunsetsu

Raishu-no getsuyobi-ni San Francisco-kara Denver-he tobi-tai-to omoi-masu(‘next’) (‘Monday’)(‘from’)(‘to’) (‘want to fly’).

Figure 3: The dependency structures of translation (J1)
Inversion

Head

Dependent

bunsetsu

bunsetsu

San Francisco-kara Denver-he tobi-tai-to omoi-masu raishu-no getsuyobi-ni(‘from’)(‘to’) (‘want to fly’)(‘next’) (‘Monday’).

Figure 4: The dependency structures of translation (J2)
• Each bunsetsu, except the last one, depends
on only one bunsetsu.
The translation (J1) is satisfied with these constraints as shown in Fig. 3. A sentence satisfying these constraints is deemed grammaticallycorrect sentence in Japanese. To meet this requirement, our method parses the dependency relations
between input chunks and generates a translation
satisfying Japanese dependency constraints.
3.2 Inversion
In this paper, we call the dependency relations
heading from right to left ”inversions”. Inversions
occur more frequently in spontaneous speech thanin written text in Japanese. That is to say, there
are some sentences in Japanese spoken languagethat do not satisfy the constraint mentioned above.
Translation (J2) does not satisfy this constraint, asshown in Fig. 4. We investigated the inversions
using the CIAIR corpus (Ohno et al., 2003) andfound the following features:
Feature 1 92.2% of the inversions are that thehead bunsetsu of the dependency relation isa predicate. (predicate inversion)
Feature 2 The more the number of dependency
relations that depend on a predicate increases,
the more the frequency of predicate inversions increases.
Feature 3 There are not three or more inversions
in a sentence.
From Feature 1, our method utilizes a predicate
inversion to retain the word order of an input sentence. It also generates a predicate when the number of dependency relations that depend on a predicate exceeds the constant R (from Feature 2). If
there are three or more inversions in the translation, the system cancels an inversion by restatinga predicate (from Feature 3).

InputParsing

Syntactic parsing
Transfer into dependency structure

Transfer

fly from San Francisco to

Denverrule

Generation

fly from San Francisco to Denver
“IN”
“NP”
“TO” “NP”

Syntactic parsing

Parsing
moduleS (want_to_fly)

Englishphrasestructure

VP (want_to_fly) *NP_subj (I)“VP”(want_to_fly) * PP(from)NP(?)

PP(to)IN(from)* NP(San Francisco)IN(to)* NP(Denver)Transfer into dependency structure

Syntactic transfer

Englishdependency
structure

Lexicon transfer

Lexicon transfer

PredicatetoIwant to“NP_subj”“VP”

Chunking

Syntactic ruletranslation rule

Chunk

POS tagging

Chunk dictionarytranslation rulewant

POS Tagging & Chunking

POS dictionary

Particle

Input words I

I want_to_fly from “San Francisco” to Denver<subject> <predicate>?

Syntactic transfer rule
Lexicon transfer rule

Syntacitc transfer &Particle translation
Predicate translation

Determine word-order of translation

Transfer
module Japanesecasestructure

Lexicon transfer

San Francisco
DenverI
want to fly<to><from>
tobu(fly)＋<hope> San Francisco<subj>
nil

OutputSan Francisco
Denvernil
tobu (fly) ＋<hope><null>
Denver?

Particle translation rule<from>
tobu(fly)<to>tobu(fly)<subj>
nil

Particle translation &Predicate translation

Figure 5: Configuration of our systemtobu(fly)＋<hope>

kara (‘from’)he (‘to’)nil

Predicate translation rule4

System Configuration

Figure 5 shows the configuration of our system.
The system translates an English speech transcript
into Japanese incrementally. It is composed ofthree modules: incremental parsing, transfer andgeneration. In the parsing module the parser determines the English dependency structure for input
words incrementally. In the transfer module, structure and lexicon transfer rules transform the English dependency structure into the Japanese casestructure. As for the generation module, the system judges whether the translation of each chunk
can be output, and if so, outputs the translationof the chunk. Figure 6 shows the processing flow
when the fragment “I want to fly from San Francisco to Denver” of （2.1）is input. In the following subsections we explain each module, referring
to Fig. 6.
4.1 Incremental Dependency Parsing
First, the system performs POS tagging for input
words and chunking (c.f. “Chunk” in Fig. 6).
Next, we explain how to parse the English
phrase structure (c.f. “English phrase structure” inFig. 6). When we parse the phrase structure for input words incrementally, there arises the problemof ambiguity; our method needs to determine only
one parsing result at a time. To resolve this problem our system selects the phrase structure of the
maximum likelihood at that time by using PCFG
(Probabilistic Context-Free Grammar) rules. To
resolve the problem of the processing time our system sets a cut-off value.

Generation
moduleJapanesedependency
structure nil tobi-tai-to omoi-masu(‘want-to-fly’)tobi-tai-to-omoi-masu

San Francisco-kara Denver-he(‘from’)(‘to’)?

Deside word-order of translation

Japanesetranslation nil

San Francisco-kara Denver-he tobi-tai-to omoi-masu(‘from’)(‘to’)(‘want-to-fly’)?

OutputDenver-hetranslation San Francisco-kara(‘from’)(‘to’)

tobi-tai-to omoi-masu(‘want-to-fly’)Figure 6: The translation flow for the fragment “Iwant to fly from San Francisco to Denver”
Furthermore, the system transforms the English
phrase structure into an English dependency structure (c.f. “English dependency structure” in Fig.
6). The dependency structure for the sentence canbe computed from the phrase structure for the input words by defining the category for each rule in
CFG, called a ”head child” (Collins, 1999). The
head is indicated using an asterisk * in the phrase
structure of Fig. 6. In the “English phrase structure,” the chunk in parentheses at each node is
the head chunk of the node that is determined by
the head information of the syntax rules. If thehead chunk (e.g. “from”) of a child node (e.g.
PP(from)) differs from that of its parent node (e.g.
VP(want-to-fly)), the head chunk (e.g. “from”) of
the child node depends on the head chunk (e.g.
“want-to-fly”) of the parent node. Some syntax
rules are also annotated with subject and object
information. Our system uses such information to
add Japanese function words to the translation of
the subject chunk or the object chunk in the generation module. To use a predicate inversion in thegeneration module the system has to recognize thepredicate of an input sentence. This system recognizes the chunk (e.g. “want to fly”) on which thesubject chunk (e.g. “I”) depends as a predicate.
4.2 Incremental Transfer
In the transfer module, structure and lexicon transfer rules transform the English dependency structure into the Japanese case structure (“Japanesecase structure” in Fig. 6). In the structure transfer,the system adds a type of relation to each dependency relation according to the following rules.
• If the dependent chunk of a dependency relation is a subject or object (e.g. “I”), then thetype of such dependency relation is “subj” or“obj”.
• If a chunk A (e.g. “San Francisco”) indirectly
depends on another chunk B (e.g. “wantto-fly”) through a preposition (e.g. “from”),
then the system creates a new dependency relation where A depends on B directly, and thetype of the relation is the preposition.
• The type of the other relations is ”null”.
In the lexicon transfer, the system transforms eachEnglish chunk into its Japanese translation.
4.3 Incremental Generation
In the generation module, the system transforms
the Japanese case structure into the Japanese dependency structure by translating a particle anda predicate. In attaching a particle (e.g. “kara”(from)) to the translation of a chunk (e.g. “SanFrancisco”), the system determines the attachedparticle (e.g. “kara” (from)) by particle translation rules. In translating a predicate (e.g. “wantto fly”), the system translates a predicate by predicate translation rules, and outputs the translationof each chunk using the method described in Section 3.
4.4 Example of Translation Process
Figure 7 shows the processing flow for the English sentence, “I want to fly from San Francisco
to Denver next Monday.” In Fig. 7 the underlined
words indicate that they can be output at that time.

5

Experiment

5.1 Outline of Experiment
To evaluate our method, we conducted a translation experiment was made as follows. We implemented the system in Java language on a 1.0-GHz

PentiumM PC with 512 MB of RAM. The OS was
Windows XP. The experiment used all 578 sentences in the ATIS corpus with a parse tree, in the
Penn Treebank (Marcus et al. 1993). In addition,
we used 533 syntax rules, which were extracted
from the corpus’ parse tree. The position of thehead child in the grammatical rule was defined according to Collins’ method (Collins, 1999).
5.2 Evaluation MetricSince an incremental translation system for spoken dialogues is required to realize a quick andinformative response to support smooth communication, we evaluated the translation results of oursystem in terms of both simultaneity and quality.
To evaluate the translation quality of our system, each translation result of our system was assigned one of four ranks for translation quality bya human translator:
A (Perfect): no problems in either information orgrammarB (Fair): easy to understand but some important
information is missing or it is grammaticallyflawedC (Acceptable): broken but understandable witheffortD (Nonsense): important information has beentranslated incorrectly
To evaluate the simultaneity of our system, we
calculated the average delay time for translating
chunks using the following expression:

Average delay time =

k dk

n

,

(1)

where dk is the virtual elapsed time from inputting
the kth chunk until outputting its translated chunk.
(When a repetition is used, dk is the elapsed timefrom inputting the kth chunk until restate its translated chunk.) The virtual elapsed time increases
by one unit of time whenever a chunk is input, nis the total number of chunks in all of the test sentences.
The average delay time is effective for evaluating the simultaneity of translation. However, it
is difficult to evaluate whether our system actually improves the efficiency of a conversation. To
do so, we measured “the speaker’ and the interpreter’s utterance time.” “The speaker’ and the interpreter ’utterance time” runs from the start timeof a speaker’s utterance to the end time of its translation. We cannot actually measure actual “theTable 1: Comparing our method (Y) with two other methods (X, Z)
Quality
Average
Speaker and interpreter
A
A+B
A+B+C
delay timeutterance time (sec)
7 (1.2%)48 (8.3%)
92 (15.9%)0
4.7
40 (6.9%)358 (61.9%)413 (71.5%)
2.79
6.0

Method
X
Y





Z

















Simultaneous translation (our method)
Consecutive translation

7

）c
se（
6
'sr noikea tlaspes na 5
a rt
fo eh 4dn tf
e od
eh n 3t eehmrof to 2
e te
im
T cna
re 1
ttu

Log approximation （our method）
Log approximation （Consecutive translation）

0
0

1

2

3

4

5

Speaker's utterance time (sec)6

7

Figure 8: The relation between the speaker’s utterance time and the time from the end time of the
speaker’s utterance to the end time of the translation
speaker’ and the interpreter’ utterance time” because our system does not include speech recognition and synthesis. Thus, the processing timeof speech recognition and transfer text-to-speechsynthesis is zero, and the speaker’s utterance time
and the interpreter’s utterance time is calculated
virtually by assuming that the speaker’s and interpreter’s utterance speed is 125 ms per mora.
5.3 Experiment Results
To evaluate the translation quality and simultaneity of our system, we compared the translation results of our method (Y) with two other methods.
One method (X) translates the input chunks withno delay time. The other method (Z) translates the
input chunks by waiting for the whole sentence to
be input, in as consecutive translation. We could
not evaluate the translation quality of the method
Z because we have not implemented the method Z.
And we virtually compute the delay time and theutterance time. Table 1 shows the estimation results of methods X, Y and Z. Note, however, that
we virtually calculated the average delay time andthe speaker’s and interpreter’s utterance times inmethod Z without translating the input sentence.
Table 1 indicates that our method Y achieved
a 55.6% improvement over method X in terms

3.79

6.4

of translation quality and a 1.0 improvement overmethod Z for the average delay time.
Figure 8 shows the relation between the
speaker’s utterance time and the time from the end
time of the speaker’s utterance to the end time of
the translation. According to Fig. 8, the longer aspeaker speaks, the more the system reduces thetime from the end time of the speaker’s utterance
to the end time of the translation.
In Section 3, we explained the constant R. Table 2 shows increases in R from 0 to 4, with theresults of the estimation of quality, the average delay time, the number of inverted sentences and thenumber of sentences with restatement. When we
set the constant to R = 2, the average delay time
improved by a 0.08 over that of method Y, andthe translation quality did not decrease remarkably. Note, however, that method Y did not utilize
any predicate inversions.
To ascertain the problem with our method,
we investigated 165 sentences whose translations
were assigned the level D when the system translated them by utilizing dependency constraints.
According to the investigation, the system generated grammatically incorrect sentences in the following cases:• There is an interrogative word (e.g. “what”，“which”) in the English sentence (64 sentences).
• There are two or more predicates in the English sentence (25 sentences).
• There is a coordinate conjunction (e.g.
“and”，“or”) in the English sentence (21 sentences).
Other cases of decreases in the translation quality
occurred when a English sentence was ill-formed
or when the system fails to parse.

6 Conclusion
In this paper, we have proposed a method for incrementally translating English spoken language
into Japanese. To realize simultaneous translation

R
0
1
2
3
4

A
8 (1.4%)14 (2.4%)
36 (6.2%)
39 (6.7%)
40 (7.0%)

Table 2: The results of each R (0 ≤ R ≤ 4)
Quality
Average
Sentences
A+B
A+B+C
delay time with inversion
152 (26.3%)
363 (62.8%)
2.51324
174 (30.1%)364 (63.0%)
2.53
289
306 (52.9%)396 (68.5%)
2.71
73344 (59.5%)
412 (71.3%)
2.79
28358 (61.9%)412 (71.3%)
2.79
3

our method utilizes the feature that word order is
flexible in Japanese, and determines the word order of a translation based on dependency structures and Japanese dependency constraints. Moreover, our method employs predicate inversion andrepetition to resolve the problem that Japanese has
a predicate at the end of a sentence. We implemented a prototype system and conducted an experiment with 578 sentences in the ATIS corpus.
We evaluated the translation results of our system in terms of quality and simultaneity, confirming that our method achieved a 55.6% improvement over the method of translating by retaining
the word order of an original with respect to translation quality, and a 1.0 improvement over the
method of consecutive translation regarding average delay time.

Acknoledgments
The authors would like to thank Prof. Dr. Toshiki
Sakabe. They also thank Yoshiyuki Watanabe,
Atsushi Mizuno and translator Sachiko Waki for
their contribution to our study.

References
F. Casacuberta, E. Vidal and J. M. Vilar. 2002. Architectures for speech-to-speech translation using
finite-state models, Proceedings of Workshop on
Speech-to-Speech Translation: Algorithms and System, pages 39-44.
M. Collins. 1999. Head-Driven Statistical Models for
Natural Language Parsing, Ph.D. Thesis, Universityof Pennsylvania,
R. Frederking, A. Blackk, R. Brow, J. Moody, andE. Stein-brecher, 2002. Field Testing the Tongues
Speech-to-Speech Machin Translation System, Proceedings of the 3rd International Conference on
Language Resources and Evaluation(LREC-2002)
pages 160-164.
H. Hoge. 2002. Project Proposal TC-STAR: Make
Speech to Speech Translation Real, Proceedings ofthe 3rd International Conference on Language Resources and Evaluation(LREC-2002), pages 136141.

Sentenceswith restatement
27
29
5
2
2

R. Isotani, K. Yamada, S. Ando, K. Hanazawa, S.
Ishikawa and K. Iso. 2003. Speech-to-Speech Translation Software PDAs for Travel Conversation, NEC
Research and Development, 44, No.2 pages 197202.
S. Kurohashi and M. Nagao. 1997. Building a JapaneseParsed Corpus while Improving the Parsing System,
Proceedings of 4th Natural Language Processing
Pacific Rim Symposium, pages 451-456.
F. Liu, Y. Gao, L. Gu and M. Picheny. 2003. Noise Robustness in Speech to Speech Translation, IBM Tech
Report RC22874.
M. P. Marcus, B. Santorini and M. A. Marcinkiewicz.
1993. Building a large annotated corpus of English: the Penn Treebank, Computational Linguistics, 19(2):310-330.
S. Matsubara and Y. Inagaki. 1997. Incremental Transfer in English-Japanese Machine Translation, IEICE Transactions on Information and Systems,(11):1122-1129.
H. Mima, H. Iida and O. Furuse. 1998. SimultaneousInterpretation Utilizing Example-based Incremental
Transfer, Proceedings of 17th International Conference on Computational Linguistics and 36th Annual
Meeting of Association for Computational Linguistics, pages 855-861.
M. Ohara, S. Matsubara, K. Ryu, N. Kawaguchi and Y.
Inagaki. 2003. Temporal Features of Cross-Lingual
Communication Mediated by Simultaneous Interpreting: An Analysis of Parallel Translation Corpus in Comparison to Consecutive Interpreting, The
Journal of the Japan Association for Interpretation
Studies pages 35-53.
T. Ohno, S. Matsubrara, N. Kawaguchi and Y. Inagaki. 2003. Spiral Construction of SyntacticallyAnnotated Spoken Language Corpus, Proceedingsof 2003 IEEE International Conference on Natural
Language Processing and Knowledge Engineering,
pages 477-483.
T. Takezawa, T. Morimoto, Y. Sagisaka, N. Campbell,
H. Iida, F. Sugaya, A. Yokoo and S. Yamamoto.
1998. A Japanese-to-English Speech Translation
System:ATR-MATRIX, Proceedings of 5th International Conference on Spoken Language Processing,pages 957-960.

I

English dependency structure

Parse tree

Input
S (?)
NP_subj (I)I
VP (?) *NP_subj (I)NP_subj (I)nil

I want_to_fly PP(?)

VP (want_to_fly) *“VP”(want_to_fly) * PP(?)
S (want_to_fly)

from

VP(?)

nil VP(?)

S (want_to_fly)

want to fly

OutputJapanese dependency structure

PP(?)

NP(?)

VP (want_to_fly) *“VP”(want_to_fly) * PP(from)nil PP(?)

PP(?)

NP(?)

I want_to_fly from
PP(?)

PP(?)

NP(?)

tobi-tai-to omoi-masu

NP(?)

PP(?)

NP(?)

NP(?)

IN(from) * NP(?)

nil

NP(?)-kara

PP(?)

NP(?)

tobi-tai-to omoi-masu

S (want_to_fly)

SanFrancisco

NP_subj (I)VP (want_to_fly) *“VP”(want_to_fly) * PP(from)

I want_to_fly from San Francisco
PP(?)

DenverNP_subj (I)nil San Francisco-kara

VP (want_to_fly) *“VP”(want_to_fly) * PP(from)NP(?)

IN(from) * NP(San Francisco) IN(to)*S (want_to_fly)NP(?)

VP (want_to_fly) *“VP”(want_to_fly) * PP(from)

PP(?)

NP(?)

I want_to_fly from San Francisco toPP(?)

NP_subj (I)

NP(?)

NP(?)

IN(from) * NP(San Francisco)
S (want_to_fly)

to

PP(?)

nil San FranciscoFrancisco-kara NP(?)-hetobi-tai-to omoi-masu

NP(?) NP(?)NP(?)

tobi-tai-to omoi-masu

I want_to_fly from San Francisco to DenverNP(?)

PP(to) NP(?)

IN(from) * NP(San Francisco) IN(to)*NP(Denver)

nil San FranciscoFrancisco-kara Denver-he

San Francisco-kara (‘from’)tobi-tai-to omoi-masu

Denver-he (‘to’)
tobi-tai-to omoimasu
NP(?) (‘want to fly’)S0(?)
S (want_to_fly)

nextMondayNP_subj (I)$(?) *
I want_to_fly from San Francisco to Denver next Monday $(?)

VP (want_to_fly) *“VP”(want_to_fly) * PP(from)

PP(to) NP(next Monday)IN(from) * NP(San Francisco) IN(to)*NP(Denver)

nil San FranciscoFrancisco-kara DenverDenver-he tobitobi-taitai-to omoiomoi-masu raishu-no getsuyobi-ni $(?)

S0($)$($) *

S (want_to_fly).

NP_subj (I)I want_to_fly from San Francisco to Denver next Monday $VP (want_to_fly) *“VP”(want_to_fly) * PP(from)raishu-no(‘next’)getsuyobi-ni(‘Monday’)PP(to) NP(next Monday)IN(from) * NP(San Francisco) IN(to)*NP(Denver)

nil San FranciscoFrancisco-kara DenverDenver-he tobitobi-taitai-to omoiomoi-masu raishu-no getsuyobi-ni $($).

Figure 7: The translation flow for “I want to fly from San Francisco to Denver next Monday.”