High-Performance Bilingual Text Alignment Using
Statistical and Dictionary Information
Masahiko Haruno Takefumi Yamazaki
NTT Communication Science Labs.
1-2356 Take Yokosuka-Shi
Kanagawa 238-03, Japan
haruno@nttkb, ntt .jp yamazaki©nttkb, ntt .jp
Abstract
This paper describes an accurate and
robust text alignment system for struc-
turally different languages. Among
structurally different languages such as
Japanese and English, there is a limitation
on the amount of word correspondences
that can be statistically acquired. The
proposed method makes use of two kinds
of word correspondences in aligning bilin-
gual texts. One is a bilingual dictionary of
general use. The other is the word corre-
spondences that are statistically acquired
in the alignment process. Our method
gradually determines sentence pairs (an-
chors) that correspond to each other by re-
laxing parameters. The method, by com-
bining two kinds of word correspondences,
achieves adequate word correspondences
for complete alignment. As a result, texts
of various length and of various genres
in structurally different languages can be
aligned with high precision. Experimen-
tal results show our system outperforms
conventional methods for various kinds of
Japanese-English texts.
1 Introduction
Corpus-based approaches based on bilingual texts
are promising for various applications(i.e., lexical
knowledge extraction (Kupiec, 1993; Matsumoto et
al., 1993; Smadja et al., 1996; Dagan and Church,
1994; Kumano and Hirakawa, 1994; Haruno et al.,
1996), machine translation (Brown and others, 1993;
Sato and Nagao, 1990; Kaji et al., 1992) and infor-
mation retrieval (Sato, 1992)). Most of these works
assume voluminous aligned corpora.
Many methods have been proposed to align bilin-
gual corpora. One of the major approaches is based
on the statistics of simple features such as sentence
length in words (Brown and others, 1991) or in
characters (Gale and Church, 1993). These tech-
niques are widely used because they can be imple-
131
mented in an efficient and simple way through dy-
namic programing. However, their main targets are
rigid translations that are almost literal translations.
In addition, the texts being aligned were structurally
similar European languages (i.e., English-French,
English-German).
The simple-feature based approaches don't work
in flexible translations for structurally different lan-
guages such as Japanese and English, mainly for the
following two reasons. One is the difference in the
character types of the two languages. Japanese has
three types of characters (Hiragana, Katakana, and
Kanji), each of which has different amounts of in-
formation. In contrast, English has only one type
of characters. The other is the grammatical and
rhetorical difference of the two languages. First, the
systems of functional (closed) words are quite differ-
ent from language to language. Japanese has a quite
different system of closed words, which greatly influ-
ence the length of simple features. Second, due to
rhetorical difference, the number of multiple match
(i.e., 1-2, 1-3, 2-1 and so on) is more than that among
European languages. Thus, it is impossible in gen-
eral to apply the simple-feature based methods to
Japanese-English translations.
One alternative alignment method is the lexicon-
based approach that makes use of the word-
correspondence knowledge of the two languages.
(Church, 1993) employed n-grams shared by two lan-
guages. His method is also effective for Japanese-
English computer manuals both containing lots of
the same alphabetic technical terms. However,
the method cannot be applied to general transla-
tions in structurally different languages. (Kay and
Roscheisen, 1993) proposed a relaxation method to
iteratively align bilingual texts using the word cor-
respondences acquired during the alignment pro-
cess. Although the method works well among Euro-
pean languages, the method does not work in align-
ing structurally different languages. In Japanese-
English translations, the method does not capture
enough word correspondences to permit alignment.
As a result, it can align only some of the two texts.
This is mainly because the syntax and rhetoric are
greatly differ in the two languages even in literal
translations. The number of confident word cor-
respondences of words is not enough for complete
alignment. Thus, the problem cannot be addressed
as long as the method relies only on statistics. Other
methods in the lexicon-based approach embed lex-
ical knowledge into stochastic models (Wu, 1994;
Chen, 1993), but these methods were tested using
rigid translations.
To tackle the problem, we describe in this
paper a text alignment system that uses both
statistics and bilingual dictionaries at the same
time. Bilingual dictionaries are now widely
available on-line due to advances in CD-ROM
technologies. For example, English-Spanish,
English-French, English-German, English-Japanese,
Japanese-French, Japanese-Chinese and other dic-
tionaries are now commercially available. It is rea-
sonable to make use of these dictionaries in bilingual
text alignment. The pros and cons of statistics and
online dictionaries are discussed below. They show
that statistics and on-line dictionaries are comple-
mentary in terms of bilingual text alignment.
Statistics Merit Statistics is robust in the sense
that it can extract context-dependent usage
of words and that it works well even if word
segmentation 1 is not correct.
Statistics Demerit The amount of word corre-
spondences acquired by statistics is not enough
for complete alignment.
Dictionaries Merit They can contain the infor-
mation about words that appear only once in
the corpus.
Dictionaries Demerit They cannot capture
context-dependent keywords in the corpus and
are weak against incorrect word segmentation.
Entries in the dictionaries differ from author to
author and are not always the same as those in
the corpus.
Our system iteratively aligns sentences by using
statistical and on-line dictionary word correspon-
dences. The characteristics of the system are as fol-
lows.
• The system performs well and is robust for var-
ious lengths (especially short) and various gen-
res of texts.
• The system is very economical because it as-
sumes only online-dictionaries of general use
and doesn't require the labor-intensive con-
struction of domain-specific dictionaries.
• The system is extendable by registering statis-
tically acquired word correspondences into user
dictionaries.
1In Japanese, there are no explicit delimiters between
words. The first task for alignment is , therefore, to
divide the text stream into words.
132
We will treat hereafter Japanese-English transla-
tions although the proposed method is language in-
dependent.
The construction of the paper is as follows. First,
Section 2 offers an overview of our alignment system.
Section 3 describes the entire alignment algorithm
in detail. Section 4 reports experimental results
for various kinds of Japanese-English texts including
newspaper editorials, scientific papers and critiques
on economics. The evaluation is performed from
two points of view: precision-recall of alignment and
word correspondences acquired during alignment.
Section 5 concerns related works and Section 6 con-
cludes the paper.
2 System Overview
Japanese text word seg~=~oa
& pos tagging
English text
Word Correspondences
............................................................... :
word anchor correspondence
counting & setting ]
1
I AUgnment
Result I
Figure 1: Overview of the Alignment System
Figure 1 overviews our alignment system. The
input to the system is a pair of Japanese and En-
glish texts, one the translation of the other. First,
sentence boundaries are found in both texts using
finite state transducers. The texts are then part-
of-speech (POS) tagged and separated into origi-
nal form words z. Original forms of English words
are determined by 80 rules using the POS infor-
mation. From the word sequences, we extract only
nouns, adjectives, adverbs verbs and unknown words
(only in Japanese) because Japanese and English
closed words are different and impede text align-
ment. These pre-processing operation can be easily
implemented with regular expressions.
2We use in this phase the JUMAN morphological
analyzing system (Kurohashi et al., 1994) for tagging
Japanese texts and Brill's transformation-based tagger
(Brill, 1992; Brill, 1994) for tagging English texts (JU-
MAN: ftp://ftp.aist-nara.ac.jp/pub/nlp/tools/juman/
Brih ftp://ftp.cs.jhu.edu/pub/brill). We would like to
thank all people concerned for providing us with the
tools.
The initial state of the algorithm is a set of al-
ready known anchors (sentence pairs). These are de-
termined by article boundaries, section boundaries
and paragraph boundaries. In the most general case,
initial anchors are only the first and final sentence
pairs of both texts as depicted in Figure 2. Pos-
sible sentence correspondences are determined from
the anchors. Intuitively, the number of possible cor-
respondences for a sentence is small near anchors,
while large between the anchors. In this phase, the
most important point is that each set of possible
sentence correspondences should include the correct
correspondence.
The main task of the system is to find anchors
from the possible sentence correspondences by us-
ing two kinds of word correspondences: statistical
word correspondences and word correspondences as
held in a bilingual dictionary 3. By using both cor-
respondences, the sentence pair whose correspon-
dences exceeds a pre-defined threshold is judged as
an anchor. These newly found anchors make word
correspondences more precise in the subsequent ses-
sion. By repeating this anchor setting process with
threshold reduction, sentence correspondences are
gradually determined from confident pairs to non-
confident pairs. The gradualism of the algorithm
makes it robust because anchor-setting errors in the
last stage of the algorithm have little effect on over-
all performance. The output of the algorithm is the
alignment result (a sequence of anchors) and word
correspondences as by-products.
English English
Eaglish
[
Japanese Japanese
Initial State
Figure 2: Alignment Process
SAdding to the bilingual dictionary of general use,
users can reuse their own dictionaries created in previous
s e s s i o n s .
133
3 Algorithms
3.1 Statistics Used
In this section, we describe the statistics used to
decide word correspondences. From many similar-
ity metrics applicable to the task, we choose mu-
tual information and t-score because the relaxation
of parameters can be controlled in a sophisticated
manner. Mutual information represents the similar-
ity on the occurrence distribution and t-score rep-
resents the confidence of the similarity. These two
parameters permit more effective relaxation than the
single parameter used in conventional methods(Kay
and Roscheisen, 1993).
Our basic data structure is the alignable sen-
tence matrix (ASM) and the anchor matrix (AM).
ASM represents possible sentence correspondences
and consists of ones and zeros. A one in ASM in-
dicates the intersection of the column and row con-
stitutes a possible sentence correspondence. On the
contrary, AM is introduced to represent how a sen-
tence pair is supported by word correspondences.
The i-j Element of AM indicates how many times
the corresponding words appear in the i-j sentence
pair. As alignment proceeds, the number of ones in
ASM reduces, while the elements of AM increase.
Let pi be a sentence set comprising the ith
Japanese sentence and its possible English corre-
spondences as depicted in Figure 3. For example, P2
is the set comprising Jsentence2, Esentence2 and
Esentencej, which means Jsentence2 has the pos-
sibility of aligning with Esentence2 or Esentencej.
The pis can be directly derived from ASM.
ex
P2
P3
Jsentence I © Esentencel
Jsentence 2 Esentence2
Jsentence 3 Esentence3
• • , ° • • , • ° • ° , ° ° , ° , , , • • • ,
PM Jsentence
Esentence N
Figure 3: Possible Sentence Correspondences
We introduce the contingency matrix (Fung and
Church, 1994) to evaluate the similarity of word oc-
currences. Consider the contingency matrix shown
Table 1, between Japanese word wjp n and English
word Weng. The contingency matrix shows: (a) the
number of pis in which both wjp, and w~ng were
found, (b) the number of pis in which just w~.g was
found, (c) the number of pis in which just wjp, was
found, (d) the number of pis in which neither word
was found. Note here that pis overlap each other
and w~,~ 9 may be double counted in the contingency
matrix. We count each w~,,~ only once, even if it
occurs more than twice in pls.
] Wjpn
Weng I a b
I c d
Table 1: Contingency Matrix
If Wjpn and weng are good translations of one an-
other, a should be large, and b and c should be small.
In contrast, if the two are not good translations of
each other, a should be small, and b and c should
be large. To make this argument more precise, we
introduce mutual information:
log prob(wjpn, Weng)
prob( w p. )prob( won9 )
The probabilities are:
a+c a+c
prob(wjpn) - a T b + c W d - Y
a+b a+b
pr ob( w eng ) - a+b+c+d - M
a a
prob( wjpn , Weng ) -- a+b+c+d- M
Unfortunately, mutual information is not reliable
when the number of occurrences is small. Many
words occur just once which weakens the statistics
approach. In order to avoid this, we employ t-score,
defined below, where M is the number of Japanese
sentences. Insignificant mutual information values
are filtered out by thresholding t-score. For exam-
ple, t-scores above 1.65 are significant at the p >
0.95 confidence level.
t ~ prob(wjpn, Weng) - prob(wjpn)prob(weng)
~/-~prob( wjpn , Weng )
3.2 Basic Alignment Algorithm
Our basic algorithm is an iterative adjustment of the
Anchor Matrix (AM) using the Alignable Sentence
Matrix (ASM). Given an ASM, mutual information
and t-score are computed for all word pairs in possi-
ble sentence correspondences. A word combination
exceeding a predefined threshold is judged as a word
correspondence. In order to find new anchors, we
combine these statistical word correspondences with
the word correspondences in a bilingual dictionary.
Each element of AM, which represents a sentence
pair, is updated by adding the number of word cor-
respondences in the sentence pair. A sentence pair
containing more than a predefined number of corre-
sponding words is determined to be a new anchor.
The detailed algorithm is as follows.
134
3.2.1 Constructing Initial ASM
This step constructs the initial ASM. If the texts
contain M and N sentences respectively, the ASM
is an M x N matrix. First, we decide a set of an-
chors using article boundaries, section boundaries
and so on. In the most general case, initial anchors
are the first and last sentences of both texts as de-
picted in Figure 2. Next, possible sentence corre-
spondences are generated. Intuitively, true corre-
spondences are close to the diagonal linking the two
anchors. We construct the initial ASM using such
a function that pairs sentences near the middle of
the two anchors with as many as O(~/~) (L is the
number of sentences existing between two anchors)
sentences in the other text because the maximum
deviation can be stochastically modeled as O(~rL)
(Kay and Roscheisen, 1993). The initial ASM has
little effect on the alignment performance so long as
it contains all correct sentence correspondences.
3.2.2 Constructing AM
This step constructs an AM when given an ASM
and a bilingual dictionary. Let thigh, tlow, Ihigh and
Izow be two thresholds for t-score and two thresholds
for mutual information, respectively. Let ANC be
the minimal number of corresponding words for a
sentence pair to be judged as an anchor.
First, mutual information and t-score are com-
puted for all word pairs appearing in a possible sen-
tence correspondence in ASM. We use hereafter the
word correspondences whose mutual information ex-
ceeds Itow and whose t-score exceeds ttow. For all
possible sentence correspondences Jsentencei and
Esentencej (any pair in ASM), the following op-
erations are applied in order.
1. If the following three conditions hold, add 3
to the i-j element of AM. (1) Jsentencei and
Esentencej contain a bilingual dictionary word
correspondence (wjpn and w,ng). (2) w~na does
not occur in any other English sentence that
is a possible translation of Jsentencei. (3)
Jsentencei and Esentencej do not cross any
sentence pair that has more than ANC word
correspondences.
2. If the following three conditions hold, add 3
to the i-j element of AM. (1) Jsentencei and
Esentencej contain a stochastic word corre-
spondence (wjpn and w~na) that has mutual
information Ihig h and whose t-score exceeds
thigh. (2) w~g does not occur in any other
English sentence that is a possible translation
of Jsentencei. (3) Jsentencei and Esentencej
do not cross any sentence pair that has more
than ANC word correspondences.
3. If the following three conditions hold, add 1
to the i-j element of AM. (1) Jsentencei and
Esentencej contain a stochastic word corre-
spondence (wjp~ and we~g) that has mutual
information Itoto and whose t-score exceeds
ttow. (2) w~na does not occur in any other
English sentence that is a possible translation
of Jsentencei. (3) Jsentencei and Esentencej
does not cross any sentence pair that has more
than ANC word correspondences.
The first operation deals with word correspon-
dences in the bilingual dictionary. The second op-
eration deals with stochastic word correspondences
which are highly confident and in many cases involve
domain specific keywords. These word correspon-
dences are given the value of 3. The third operation
is introduced because the number of highly confi-
dent corresponding words are too small to align all
sentences. Although word correspondences acquired
by this step are sometimes false translations of each
other, they play a crucial role mainly in the final
iterations phase. They are given one point.
3.2.3 Adjusting ASM
This step adjusts ASM using the AM constructed
by the above operations. The sentence pairs that
have at least ANC word correspondences are deter-
mined to be new anchors. By using the new set of
anchors, a new ASM is constructed using the same
method as used for initial ASM construction.
Our algorithm implements a kind of relaxation by
gradually reducing flow, Izow and ANC, which en-
ables us to find confident sentence correspondences
first. As a result, our method is more robust than
dynamic programing techniques against the shortage
of word-correspondence knowledge.
4 Experimental Results
In this section, we report the result of experiments
on aligning sentences in bilingual texts and on sta-
tistically acquired word correspondences. The texts
for the experiment varied in length and genres as
summarized in Table 2. Texts 1 and 2 are editorials
taken from 'Yomiuri Shinbun' and its English ver-
sion 'Daily Yomiuri'. This data was distributed elec-
trically via a WWW server 4. The first two texts clar-
ify the systems's performance on shorter texts. Text
3 is an essay on economics taken from a quarterly
publication of The International House of Japan.
Text 4 is a scientific survey on brain science taken
from 'Scientific American' and its Japanese version
'Nikkei Science '5. Jpn and Eng in Table2 represent
the number of sentences in the Japanese and English
texts respectively. The remaining table entries show
4The Yomiuri data can
be obtained from www.yomiuri.co.jp. We would like to
thank Yomiuri Shinbun Co. for permitting us to use the
data.
~We obtained the data from paper version of the mag-
azine by using OCR. We would like to thank Nikkei Sci-
ence Co. for permitting us to use the data.
135
categories of matches by manual alignment and in-
dicate the difficulty of the task.
Our evaluation focuses on much smaller texts than
those used in other study(Brown and others, 1993;
Gale and Church, 1993; Wu, 1994; Fung, 1995; Kay
and Roscheisen, 1993) because our main targets are
well-separated articles. However, our method will
work on larger and noisy sets too, by using word
anchors rather than using sentence boundaries as
segment boundaries. In such a case, the method
constructing initial ASM needs to be modified.
We briefly report here the computation time of
our method. Let us consider Text 4 as an exam-
ple. After 15 seconds for full preprocessing, the
first iteration took 25 seconds with tto~ = 1.55 and
Izow = 1.8. The rest of the algorithm took 20 sec-
onds in all. This experiment was performed on a
SPARC Station 20 Model tIS21. From the result,
we may safely say that our method can be applied
to voluminous corpora.
4.1 Sentence Alignment
Table 3 shows the performance on sentence align-
ments for the texts in Table 2. Combined, Statis-
tics and Dictionary represent the methods using
both statistics and dictionary, only statistics and
only dictionary, respectively. Both Combined and
Dictionary use a CD-ROM version of a Japanese-
English dictionary containing 40 thousands entries.
Statistics repeats the iteration by using statistical
corresponding words only. This is identical to Kay's
method (Kay and Roscheisen, 1993) except for the
statistics used. Dictionary performs the iteration
of the algorithm by using corresponding words of
the bilingual dictionary. This delineates the cover-
age of the dictionary. The parameter setting used
for each method was the optimum as determined by
empirical tests.
In Table 3, PRECISION delineates how many of
the aligned pairs are correct and RECALL delineates
how many of the manual alignments we included
in systems output. Unlike conventional sentence-
chunk based evaluations, our result is measured on
the sentence-sentence basis. Let us consider a 3-1
matching. Although conventional evaluations can
make only one error from the chunk, three errors
may arise by our evaluation. Note that our evalua-
tion is more strict than the conventional one, espe-
cially for difficult texts, because they contain more
complex matches.
For Text 1 and Text 2, both the combined
method and the dictionary method perform much
better than the statistical method. This is ob-
viously because statistics cannot capture word-
correspondences in the case of short texts.
Text 3 is easy to align in terms of both the com-
plexity of the alignment and the vocabularies used.
All methods performed well on this text.
For Text 4, Combined and Statistics perform
1 Root out guns at all costs 26 28 24 2 0 0
2 Economy ]acing last hurdle 36 41 25 7 2 0
3 Pacific Asia in the Post-Cold-War World 134 124 114 0 10 0
4 Visualizing the Mind 225 214 186 6 15 1
II Combined
Text PRECISION I RECALL
1 96.4% 96.3%
2 95.3% 93.1%
3 96.5% 97.1%
4 91.6% 93.8%
much better than Dictionary. The reason for this is
that Text 4 concerns brain science and the bilingual
dictionaries of general use did not contain domain
specific keywords. On the other hand, the combined
and statistical methods well capture the keywords
as described in the next section. Note here that
Combined performs better than Statistics in the
case of longer texts, too. There is clearly a limitation
in the amount of word correspondences that can be
captured by statistics. In summary, the performance
of Combined is better than either Statistics or
Dictionary for all texts, regardless of text length
and the domain.
4.2 Word Correspondence
In this section, we will demonstrate how well the pro-
posed method captured domain specific word corre-
spondences by using Text 4 as an example. Table 4
shows the word correspondences that have high mu-
tual information. These are typical keywords con-
cerning the non-invasive approach to human brain
analysis. For example, NMR, MEG, PET, CT, MRI
and functional MRI are devices for measuring brain
activity from outside the head. These technical
terms are the subjects of the text and are essential
for alignment. However, none of them have their
own entry in the bilingual dictionary, which would
strongly obstruct the dictionary method.
It is interesting to note that the correct Japanese
translation of 'MEG' is ' ~{i~i~]'. The Japanese mor-
phological analyzer we used does not contain an en-
try for ' ~i~i[~' and split it into a sequence of three
characters ' ~',' ~' and ' []'. Our system skillfully
combined ' ~i' and ' []' with 'MEG', as a result of
statistical acquisition. These word correspondences
greatly improved the performance for Text 4. Thus,
the statistical method well captures the domain spe-
cific keywords that are not included in general-use
bilingual dictionaries. The dictionary method would
yield false alignments if statistically acquired word
Table 2: Test Texts
Statistics
PRECISION RECALL
65.0% 48.5%
61.3% 49.6%
87.3% 85.1%
82.2% 79.3%
Table 3: Result of Sentence Alignment
136
Dictionary
PRECISION RECALL
89.3% 88.9%
87.2% 75.1%
86.3% 88.2%
74.3% 63.8%
correspondences were not used.
Although these word correspondences are very ef-
fective for sentence alignment task, they are unsat-
isfactory when regarded as a bilingual dictionary.
For example, ' 7 7 Y ~' ~ ~ ~n.MR I ' in Japanese
is the translation of 'functional MRI'. In Table 4, the
correspondence of these compound nouns was cap-
tured only in their constituent level. (Haruno et al.,
1996) proposes an efficient n-gram based method to
extract bilingual collocations from sentence aligned
bilingual corpora.
5 Related Work
Sentence alignment between Japanese and English
was first explored by Sato and Murao (Murao, 1991).
They found (character or word) length-based ap-
proaches were not appropriate due to the structural
difference of the two languages. They devised a
dynamic programming method based on the num-
ber of corresponding words in a hand-crafted bilin-
gual dictionary. Although some results were promis-
ing, the method's performance strongly depended on
the domain of the texts and the dictionary entries.
(Utsuro et al., 1994) introduced a statistical post-
processing step to tackle the problem. He first ap-
plied Sato's method and extracted statistical word
correspondences from the result of the first path.
Sato's method was then reiterated using both the ac-
quired word correspondences and the hand-crafted
dictionary. His method involves the following two
problems. First, unless the hand-crafted dictionary
contains domain specific key words, the first path
yields false alignment, which in turn leads to false
statistical correspondences. Because it is impossible
in general to cover key words in all domains, it is
inevitable that statistics and hand-crafted bilingual
dictionaries must be used at the same time.
Japanese
[ English Mutual InFormation I
~)T.,t.~4"recordin~
rea~
3.68
3.51
neuron 3.51
film 3.51
~lucose 3.51
incrense 3.~1
MEG 3.51
resolution 3.43
electrical 3.43
group 3.39
recordin~
3.39
electrical 3.39
~:enerate 3.32
provide 3.33
MEG 3.33
noun 3.17
NMB.
NMB. 3.17
functional 3.17
equipment 3.17
organ
3.15
compound
3.10
water
3.10
radioactive
3.10
PET
PET
3.10
spatial
:}.10
such
3.10
metabolism
3.06
~5
verb
3.04
scientist
2.9E
wnter
2.98
water
2.98
mappin|
2.92
take
2.92
university
2.92
thousht
2.90
compound
2,82
label
2,82
N5
task
2,82
radioactivity
2.77
visual
2.77
noun
2.77
N5
si|nal
present
2.77
2.72
I) 7"/L,~Z 4 .&
~xY
time
dan~6~e
2.69
2.69
a.ut oradiogrsphy
2.67
ability
2.63
CT
CT
2.63
,b
MR !
auditory
mental
MRI
2.19
2.05
1.8
Table 4: Statistically Acquired Keywords
The proposed method involves iterative alignment
which simultaneously uses both statistics and a
bilingual dictionary.
Second, their score function is not reliable espe-
cially when the number of corresponding words con-
tained in corresponding sentences is small. Their
method selects a matching type (such as 1-1, 1-2
and 2-1) according to the number of word correspon-
dences per contents word. However, in many cases,
there are a few word translations in a set of corre-
sponding sentences. Thus, it is essential to decide
sentence alignment on the sentence-sentence basis.
Our iterative approach decides sentence alignment
level by level by counting the word correspondences
between a Japanese and an English sentence.
137
(Fung and Church, 1994; Fung, 1995) proposed
methods to find Chinese-English word correspon-
dences without aligning parallel texts. Their mo-
tivation is that structurally different languages such
as Chinese-English and Japanese-English are diffi-
cult to align in general. Their methods bypassed
aligning sentences and directly acquired word cor-
respondences. Although their approaches are ro-
bust for noisy corpora and do not require any in-
formation source, aligned sentences are necessary
for higher level applications such as well-grained
translation template acquisition (Matsumoto et as.,
1993; Smadja et al., 1996; Haruno et al., 1996)
and example-based translation (Sato and Nagao,
1990). Our method performs accurate alignment for
such use by combining the detailed word correspon-
dences: statistically acquired word correspondences
and those from a bilingual dictionary of general use.
(Church, 1993) proposed char_align that makes
use of n-grams shared by two languages. This
kind of matching techniques will be helpful in our
dictionary-based approach in the following situation:
Entries of a bilingual dictionary do not completely
match the word in the corpus but partially do. By
using the matching technique, we can make the most
of the information compiled in bilingual dictionaries.
6 Conclusion
We have described a text alignment method for
structurally different languages. Our iterative
method uses two kinds of word correspondences at
the same time: word correspondences acquired by
statistics and those of a bilingual dictionary. By
combining these two types of word correspondences,
the method covers both domain specific keywords
not included in the dictionary and the infrequent
words not detected by statistics. As a result, our
method outperforms conventional methods for texts
of different lengths and different domains.
Acknowledgement
We would like to thank Pascale Fung and Takehito Ut-
suro for helpful comments and discussions.
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