EXPERIMENTS AND PROSPECTS OF 
EXAMPLE-BASED MACHINE TRANSLATION 
Eiichiro SUMITA* 
and 
Hitoshi HDA 
ATR Interpreting Telephony Research Laboratories 
Sanpeidani, Inuidani, Seika-cho 
Souraku-gun, Kyoto 619-02, JAPAN 
ABSTRACT 
EBMT (Example-Based Machine Translation) 
is proposed. EBMT retrieves similar examples 
(pairs of source phrases, sentences, or 
texts and their translations) from a d~t.hase of 
examples, adapting the examples to translate a new 
input. EBMT has the following features: (1) It is 
easily upgraded simply by inputting appropriate 
examples to the database; (2) It assigns a reliability 
factcr to the translation result; (3) It is acoelerated 
effectively by both indexing and parallel computing; 
(4) It is robust because of best-match reasoning; ~d 
(5) It well utilizes translator expertise. A prototype 
system has been implemented to deal with a difficult 
translation problem for conventional Rule-Based 
Machine Translation (RBMT), i.e., translating 
Japanese noun phrases of the form "N~ no N2" into 
English. The system has achieved about a 78% 
success rate on average. This paper explains the basic 
idea of EBMT, illustrates the experiment in detail, 
explains the broad applicability of EBMT to several 
difficult translation problems for RBMT and 
discusses the advantages of integrating EBMT with 
RBMT. 
1 INTRODUCTION 
Machine Translation requires handcmt~ and 
complicated large-scale knowledge (Nirenburg 1987). 
Conventional machine translation systems use 
rules as the knowledge. This framework is 
called Rule-Based Machine Translation 
(RBMT). It is difficult to scale up from a toy 
program to a practical system because of the problem 
of building such a lurge-scale rule-base. It is also 
difficult to improve translation performance because 
the effect of adding a new rule is hard to anticipate, 
and because translation using a large-scule rule-based 
system is time-consuming. Moreover, it is difficult 
to make use of situational or domain-specific 
information for translation. 
their translations) has been implemented as 
the knowledge (Nagao 1984; Sumita and 
Tsutsumi 1988; Sato and Nagao 1989; Sadler 1989a; 
Sumita et al. 1990a, b). The translation mechanism 
retrieves similar examples from the database, 
adapting the examples to Wanslate the new source 
text. This framework is called Example-Based 
Machine Translation (EBMT). 
This paper focuses on ATR's linguistic 
database of spoken Japanese with English 
translations. The corpus contains conversations about 
international conference registration (Ogura et al. 
1989). Results of this study indicate that EBMT is a 
breakthrough in MT technology. 
Our pilot EBMT system translates Japanese 
noun phrases of the form '~1 x no N2" into English 
noun phrases. About a 78% success rate on 
average has been achieved in the 
experiment, which i s considered to 
outperform RBMT. This rate cm be improved as 
discussed below. 
Section 2 explains the basic idea of EBMT. 
Section 3 discusses the broad applicability of EBMT 
and the advantages of integrating it with RBMT. 
Sections 4 and 5 give a rationale for section 3, i.e., 
section 4 illustrates the experiment of translating 
noun phrases of the form "Nt no N2" in detail, and 
section 5 studies other phenomena through actual 
dam from our corpus. Section 6 concludes this paper 
with detailed comparisons between RBMT and 
EBMT. 
2 BASIC IDEA OF EBMT 
2.1 BASIC FLOW 
In this section, the basic idea of EBMT, 
which is general and applicable to many phenomena 
dealt with by machine translation, is shown. 
In order to conquer these problems in machine 
translation, a database of examples (pairs of 
source phrases, sentences, or texts and 
* Currently with Kyoto University 
Figure 1 shows the basic flow of EBMT 
using translation of "kireru"\[cut/be sharp\]. From 
here on, the literal English translations are 
bracketed. 
(1) and (2) me examples (pairs of 
Japanese sentences and their English 
185 
translations) in the database. 
Examples similar to the Japanese 
input sentence are retrieved in the following 
manner. Syntactically, the input is similar to 
Japanese sentences (1) and (2). However, 
semantically, "kachou" \[chief\] is far from "houchou" 
\[kitchen knife\]. But, "kachou" \[chief\] is semantically 
similar to "kanojo" \[she\] in that both are people. In 
other words, the input is similar to example sentence 
(2). By mimicking the similar example (2), we 
finally get "The chief is sharp". 
Although it is possible to obtain the same 
result by a word selection rule using fme-tuned 
semantic restriction, note that translation here is 
obtained by retrieving similar examples to the input. 
• Example Database 
(data for "kireru'\[cut / be sharp\]) 
(1) houchou wa klrsru -> The kitchen knife cuts. 
(2) kanojo wa kireru -> She Is sharp. 
• Input 
kachouwa klreru o>? 
• Retrieval of similar examples 
(Syntax) Input = (1), (2) 
(Semantics) kachou/== houehou 
kachou ,= kanojo 
(Total) Input == (2) 
• OUt0Ut -> The chief Is ~ h a r D, 
Figure I Mimicking Similar Examples 
2.2 DISTANCE 
Retrieving similar examples to the input is 
done by measuring the distance of the input to 
each of examples. The smaller a distance is, the 
more similar the example is to the input. To define 
the best distance metric is a problem of EBMT not 
yet completely solved. However, one possible 
definition is shown in section 4.2.2. 
From similar examples retrieved, EBMT 
generates the most likely translation with a 
reliability factor based on distance and frequency. If 
there is no similar example within the given 
threshold, EBMT tells the user that it cannot 
translate the input. 
3 BROAD APPLICABILITY AND 
INTEGRATION 
3.1 BROAD APPLICABILITY 
EBMT is applicable to many linguistic 
phenomena that are regarded as difficult to translate in 
conventional RBMT. Some are well-known among 
researchers of natural language processing and others 
have recently been given a great deal of attention. 
When one of the following conditions holds 
true for a linguistic phenomenon, RBMT is less 
suitable than EBMT. 
(Ca) Translation rule formation is 
difficult. 
(Cb) The general rule cannot accurately 
describe phenomena because it 
represents a special case, e.g., idioms. 
(Cc) Translation cannot be made in a 
compositional way from target words 
(Nagao 1984; Nitta 1986; Sadler 1989b). 
This is a list (not exhaustive) of phenomena 
in J-E translation that are suitable for EBMT: 
• optional cases with a case particle 
( "- de", "~ hi",...) 
• subordinate conjunction ("- ba -", "~ nagara -", 
"~ tara -",...,"- baai ~",...) 
• noun phrases of the form '~1 no N2" 
• sentences of the form "N~ wa N 2 da" 
• sentences lacking the main verb (eg. sentences of 
the form "~ o-negaishimasu") 
• fragmental expressions Chai", "sou-desu", 
"wakarimashita",...) (Furuse et al. 1990) 
• modality represented by the sentence ending 
C-tainodesuga", "~seteitadakimasu", ...) 
(Furuse et al. 1990) 
• simple sentences (Sato and Nagao 1989) 
This paper discusses a detailed experiment for 
"N~ no N2" in section 4 and prospects for other 
phenomena, "N1 wa N2 da" and "~ o-negaishimasu" 
in section 5. 
Similar phenomena in other language 
pairs can be found. For example, in Spanish to 
English translation, the Spanish preposition "de", 
with its broad usage like Japanese "no", is also 
effectively Iranslated by EBMT. Likewise, in German 
to English translation, the German complex noun is 
also effectively translated by EBMT. 
3.2 INTEGRATION 
It is not yet clear whether EBMT can or 
should deal with the whole process of translation. We 
assume that there are many kinds of phenomena. 
Some are suitable for EBMT, while others are 
suitable for RBMT. 
Integrating EBMT with RBMT i s 
expected to be useful. It would be more 
acceptable for users if RBMT were first introduced as 
a base system, and then incrementally have its 
translation performance improved by attaching 
EBMT components. This is in the line with the 
proposal in Nagao (1984). Subsequently, we 
proposed a practical method of integration in 
186 
previous papers (Sumita et al. 1990a, b). 
4 EBMT FOR "N x no Nz" 
4.1 THE PROBLEM 
"N~ no N2" is a common Japanese noun 
phrase form. "no" in the "Nt no Nz" is a Japanese 
adnominal particle. There are other variants, 
including "deno", "karano", "madeno" and so on. 
Roughly speaking, Japanese noun phrases of 
the form "N~ no N2" correspond to English noun 
phrases of the form "N2 of N:" as shown in the 
examples at the top of Figure 2. 
Japanese English 
youka n o gogo the afternoon o f the 8th 
kaigi no mokuteki the object o f the conference 
.......................................... 
kaigi n o sankaryou the application fee for the conf. 
?the application fee o fthe conf. 
kyoutodenokaigi theconf, in Kyoto 
.'/the conf. o f Kyoto 
isshukan no kyuka a week' s holiday 
?the holiday o f a week 
mittsu no hoteru three hotels 
*hotels o fthree 
Figure 2 Variations in Translation of "N1 no N2" 
However, "N2 of Nt" does not always provide 
a natural translation as shown in the lower examples 
in Figure 2. Some translations are too broad in 
meaning to interpret, others axe almost 
ungrammatical. For example, the fourth one, "the 
conference of Kyoto", could be misconstrued as "the 
conference about Kyoto", and the last one, "hotels of 
three", is not English. Natural translations often 
require prepositions other than "of", or no 
preposition at all. In only about one-fifth of "N~ no 
N2" occurrences in our domain, "N2 of Nt" would be 
the most appropriate English translation. We cannot 
use any particular preposition as an effecdve de.fault 
value. 
No rules for selecting the most appropriate 
translation for "N~ no N2" have yet been found. In 
other words, the condition (Ca) in section 3.1 holds. 
Selecting the translation for '~1~ no N2" is still an 
important and complicated problem in J-E 
translation. 
In contrast with the preceding research 
analyzing "NI no N2" (Shimazu et al. 1987; Hirai and 
Kitahashi 1986), deep semantic analysis is avoided 
because it is assumed that translations appropriate 
for given domain can be obtained using 
domain-specific examples (pairs of source md target 
expressions). EBMT has the advantage that it can 
directly return a translation by adapting examples 
without reasoning through a long chain of rules. 
4.2 IMPLEMENTATION 
4.2.1 OVERVIEW 
The EBMT system consists of two databases: 
an example database and a thesaurus; and also three 
translation modules: analysis, example-based transfer, 
and generation (Figure 3). 
Examples (pairs of source phrases 
and their translations) are extracted from ATR's 
linguistic database of spoken Japanese with English 
translations. The corpus contains conversations about 
registering for an international conference (Ogura 
1989). 
Example 
Database 
(1) Analysis I 
(2) Example-Based 
Transfer 
Thesaurus 
I (3) Generation I 
Figure 3 System Configuration 
The thesaurus is used in calculating 
the semantic distance between the content 
words in the input and those in the 
examples. It is composed of a hierarchical structure 
in accordance with the thesaurus of everyday Japanese 
written by Ohno and Hamanishi (1984). 
Analysis 
kyouto deno kaigi 
Example-Based Transfer 
d Japanese English 
0.4 toukyou deno taizai the stay in Tokyo 
0.4 honkon deno taizai the stay in Hongkong 
0.4 toukyou deno go-taizai the stay in Tokyo 
1.0 oosaka no kaigi the conf. in Osaka 
1.0 toukyou no kaigi the conf. in Tokyo 
Generation 
the conf. in Kyoto 
Figure 4 Translation Procedure 
Figure 4 illustrates the translation procedure 
with an actual sample. First, morphological analysis 
is performed for the input phrase,"kyouto\[Kyoto\] 
deno kaigi \[conference\]". In this case, syntactical 
187 
analysis is not necessary. Second, similar examples 
are retrieved from the database. The top five similar 
examples are shown. Note that the top three 
examples have the same distance and that they are all 
translated with "in". Third, using this rationale, 
EBMT generates "the conference in Kyoto". 
4.2.2 DISTANCE CALCULATION 
The distance metric used when retrieving 
examples is essential and is explained hem in detail. 
we suppose that the input and examples (I, E) 
in the d~tAl~ase ~ r~ted in the same data 
structure, i.e., the list of words' syntactic and 
semantic attribute values (refeaxed to as and I~, E~) for 
each phrase. 
The attributes of the current target, "Nt no 
N2" , 8~ as follows: 1) for the nouns "NI" and "N2": 
the lexical subcategory of the noun, the existence of 
a prefix or suffix, and its semantic code in the 
thesaurus; 2) for the adnominal particle "no": the 
kinds of variants, "deno", "karano", "madeno" and so 
on. Here, for simplicity, only the semantic code and 
the kind of adnominal a=e considered. 
Distances ae calculated using the following 
two expressions (Sumita et al. 1990a, b): 
(1) d(I,E)= d(li,Ei) "w i 
i 
(2) wi=,~// ~. ( freq. of t. p. when Ei=li ) 2 
t.p. 
The attribute distance, d(li, E.~ end the weight 
of attribute, w~ are explained in the following 
sections. Each Iranslation pattern (t.p.) is abstracted 
from an example md is stored with the example in 
the example d~mhase \[see Figure 6\]. 
(a) ATTRIBUTE DISTANCE 
For the attribute of the adnominal particle 
"no", the distance is 0 or 1 depending on whether or 
not they match exactly, for example, 
d("deno","deno") = 0 and d("deno", "no") = 1. 
For semantic attributes, however, the distance 
varies between 0 and 1. Semantic distance d(0 < 
d < 1)is determined by the Most Specific 
Common Abstractlon(MSCA) (Kolodner and 
Riesbeck 1989) obtained from the thesaurus 
abstraction hierarchy. When the thesaurus is (n+l) 
layered, (k/n) is assigned to the concepts in the k-th 
layer from the bottom. For example, as shown with 
the broken line in Figure 5, the MSCACkaigi '' 
\[conference\], "taizai" \[stay\]) is "koudou" \[actions\] and 
the distance is 2/3. Of course, 0 is assigned when the 
MSCA is the bottom class, for instance, 
MSCACkyouto"\[Kyoto\], "toukyou" \[Tokyo\])= 
"timei"\[placc\], or when nouns are identical ( 
MSCA(N, N) for any N). 
Thesaurus Root 
\[actions\] 
(1/3) 
oural 
omings 
goings\] 
setsumei 
tions\] 
(o) I 
kaisetsu 
\[commen- 
tary\] 
//.,. ",\ 
\[ taizai I I hatchaku 
I \[stays\] II \[arrivals & \[meetingslJ 
J J Jdepartures', II :o) 
, ili i kaigi taizai touchaku 
\[conference\] \[stay\] \[arrive\] 
Figure 5 Thesaurus(portion) 
(b) WEIGHT OF ATTRIBUTE 
The weight of the attribute is the 
degree to which the attribute influences 
the selection of the translation 
pattern(t.p.). We adopt the expression (2) used 
by Stanfill and Waltz (1986) for memory-based 
reasoning, to implement the intuition. 
t.p. freq. 
B in A 12/27 
AB 4/27 
B from A 2/27 
BA 2/27 
BtoA 1/27 
(E l=timei) 
\[place/ 
t.p. freq. 
B in A 313 
(E2=deno) 
\[in/ 
t.p. freq. 
B 9/24 
AB 9/24 
B in A 2/24 
A's B 1124 
BonA 1/24 
(E3=soudan) 
\[meetings\] 
Figure 6 Weight of the i-th attribute 
188 
In Figure 6, all the examples whose E2 = 
"deno" aze translated with the same preposition, 
"in". This implies that when El= "deno", E2 is an 
attribute which heavily influences the selection of the 
translation pattern. In contrast to this, the translation 
patterns of examples whose E1 = "timei"\[place\], =e 
varied. This implies that when E1 -- "timei"\[place\], 
E~is an attribute which is less influential on the 
selection of the translation pattern. 
According to the expression (2), weights for 
attributes, E~, E2 and E3me as follows: 
W1=,~(12/27) 2+(4127 ) 2+...+(1/27)2 = 0.49 
W2=,,~(3/3) 2 = 1.0 
w3=,~(9/24 ) 2+(9124 ) 2+. ..+(1/24) 2 ,= 0.54 
(C) TOTAL DISTANCE 
The distance between the input and the first 
example shown in Figure 4 is calculated using the 
weights in section 4.2.2 Co), attribute distances as 
explained in section 4.2.2 (a) and expression (1) at 
the beginning of section 4.2.2. 
d( "kyouto'\[Kyoto\] "deno'\[in\] "kaigi'\[ conference\], 
"toukyou'\[Tokyo\] "deno'\[in\] "taizai'\[stay\]) 
,= d('kyouto','toukyou" )*0.49+ 
d('deno",'deno')*1.0+ 
d('kaigi", "taizai')*0.54 
= 0"0.49+0"1.0+2/3"0.54 = 0.4 
4.3 EXPERIMENTS 
The current number of words in the corpus is 
about 300,000 and the number of examples is 2,550. 
The collection of examples from another domain is 
in progress. 
4.3.1 JACKKNIFE TEST 
In ~ to roughly estimate translation 
performance, a jackknife experiment was conducted. 
We partitioned the example database(2,550) in groups 
of one hundred, then used one set as input(100) and 
translated them with the rest as an example database 
(2,450). This was repeated 25 times. 
Figure 7 shows that the average success 
rate is 78%, the minimum 70% and the 
maximum 89% \[see section 4.3.4\]. 
It is difficult to fairly compare this result 
with the success rate of the existing MT system. 
However, it is believed that current conventional 
systems can at best output the most common 
translation pattern, for example, "B of A", as the 
default. In this case, the average success rate may 
only be about 20%. 
success(%) MAXIMUM(89%) 
100 
80 ~~_ ,.. 
60 AVERAGE(78%) 
MINIMUM(70%) 
0 I I 
1 11 21 
test number 
Figure 7 Result of Jackknife Test 
40 
20 
4.3.2 SUCCESS RATE PER 
NUMBER OF EXAMPLES 
Figure 8 shows the relationship between the 
success rate and the number of examples. Of the 
twenty-five cases in the previous jackknife test, three 
are shown: maximum, average, and minimum. This 
graph shows that, in general, the more examples 
we have, the better the quality \[see section 
4.3.4\]. 
success(%) MAXIMUM 
80 t| /J~~~,~'~'~''--/-- .,, ~s' AVERAGE 
70 . _. - .. ---.'''--- 
50 
1 11 21 
no. of examples (x 100) 
Figure 8 Success Rate per No. of Examples 
189 
4.3.3 SUCCESS RATE PER 
DISTANCE 
Figure 9 shows the relationship between the 
success rate and the distance between the input and 
the most similar examples retrieved. 
This graph shows that in general, the 
smaller the distance, the better the quality. 
In other words, EBMT assigns the distance between 
the input and the retrieved examples us a reliability 
factor. 
SUCCESS 
0.9 r 1592/1790 
0.8 
0.7 
0.6 
0.5 
0.4 
0.3 
0.2 
0.1 
0 
23137 100 / 169 
• 19/33 
• = • 35167 
951162 • 
74/148 8/24 
7/14 
3/56 
E• I I I I I 
0 0.2 0.4 0.6 0.8 1 
distance 
Figure 9 Success Rate per Distance 
4.3.4 SUCCESSES AND FAILURES 
The following represents successful results: 
(1) the noun phrase "kyouto-eki \[Kyoto-station\] no 
o-mise \[store\]" is wansta_!ed according to the 
translation pattern "B at A" while the similar noun 
phrase, "kyouto\[Kyoto\] no shiten \[branch\]" is 
translated according to the translation pattern "13 in 
A"; (2) the noun phrase of the form "N~ no hou" is 
translated according to the translation pattern "A", in 
other words, the second noun is omitted. 
We ~e now studying the results carefully ~d 
are striving to improve the success rate. 
(a) About half of the failures are caused by a lack of 
similar examples. They are easily solved by adding 
appropriate examples. 
Co) The rest are caused by the existence of similar 
examples: (1) equivalent but different examples are 
retrieved, for instance, those of the form, "B of A" 
and "AB" for "rolm-gatsu \[June\] no futsu-ka 
\[second\]". This is one of the main reasons the graphs 
(Figure 7 and 8) show an up-and-down pattern. They 
can be regarded as a correct translation or the distance 
calculation may be changed to handle the problem; 
(2) Because the current distance calculation is 
inadequate, dissimilar examples are retrieved. 
5 PHENOMENA OTHER THAN 
"N 1 no Nz" 
This section studies the phenomena, "N1 wa 
N2 da" and "- o-negaishimasu" with the same corpus 
used in the previous section. 
5.1 "N x wa N~ da" 
A sentence of the form "N\] wa N2 da" is 
called a "da" sentence. Here "N{' and '~2" ~e nouns, 
"wa" is a topical particle, and "da" is a kind of verb 
which, roughly speaking, is the English copula "be". 
The correspondences between "da" sentences 
and the English equivalents are exemplified in Figure 
10. Mainly, "N~ wa N2 da" corresvonds to '~ be Nz" 
like (a-l) - (a-4). 
However, sentences like (b) - (e) cannot be 
translated according to the translation pattern ,N~ be 
N2". In example (d), there is no Japanese counterpart 
of "payment should be made- by". The English 
sentence has a modal, passive voice, the verb make, 
and its object, payment, while the Japanese sentence 
has no such correspondences. This translation cannot 
be made in a compositional way from the target 
words which ale selected from a normal dictionary. It 
is difficult to formulate rules for the translation and 
to explain how the translation is made. The 
conditions (Ca) and (Co) in section 3.1 hold true. 
Conventional approaches lead to the 
understanding of"da" sentences using contextual and 
exwa-linguistic information. However, many 
translations exist that are the result of human 
translators' understanding. Translation can be made 
by mimicking such similar examples. 
Example (e) is special, i.e., idiomatic. The 
condition (Co) in section 3.1 holds. 
(a) NI be N= 
watashi\[I\] 
kochira\[this\] 
denwa-bango\[tel-no.\] 
sanka-hi\[fee\] 
(b) N, cost N= 
yokoushuu\[proc.\] 
N, 
jonson\[Johnson\] 
jim ukyoku\[secretariat\] 
06-951-0866106-951-0866\] 
85,000-en\[85,000 yen\] 
30,000-en\[30,000 yen\] 
(c) for N,, the fee is N= 
kigyou\[companies\] 85,000-en\[85,000 yen\] 
(d) payment should be made by N= 
hiyou\[fee\] ginnkou-furikomi 
\[bank-transfer\] 
(e) the conference will end on N= 
saishuu-bi\[final day\] 10qatsu12nichi\[Oct. 12th\] 
Figure 1 0 Examples of "N1 wa N2da" 
The distribution of N\] and N2 in the examples 
190 
of our corpus vary for each case. Attention should be 
given to 2-tuples of nouns, (N1, N2). N2s of (a-4), (13) 
and (c) are similar, i.e., both mean "prices". However 
N~s are not similar to each other. Nls of (a-4) and (d) 
~e similar, i.e., both mean "fee". However, the N2s 
~e not similar to each other. Thus, EBMT is 
applicable. 
5.2 "~ o-negaishimasu" 
Figure 11 exemplifies the conespondences 
between sentences of the form "~ o-negaishimasu" 
and the English equivalents. 
(a) may I speak to N 
(b) please give me N 
(c) please pay by N 
(d) yes, please 
(e) thank You 
Figure 11 
jim ukyoku\[secretariat\] o 
o-negaishlmasu 
go-ju usyo\[add ress\] o... 
genkin\[cash\] de... 
hal... 
voroshiku... 
Examples of "~ o-negaishimasu" 
Translations in examples (b) and (c) are 
possible by finding substitutes in Japanese for give 
me and pay by, respectively. The conditions (Ca) 
and (Cc) in section 3.1 hold. Usually, this kind of 
supplement is done by contextual analysis. However, 
the connection between the missing elements and the 
noun in the examples is strong enough to reuse, 
because it is the product of a combination of 
translator expertise and domain specific restriction. 
Examples (a), (d) and (e) are idiomatic expressions. 
The condition (Cb) holds. The distribution of the 
noun and the particle in the examples of our corpus 
varies for each case in the same way as in the "da" 
sentence. EBMT is applicable. 
6 CONCLUDING REMARKS 
Example-Based Machine Translation (EBMT) 
has been proposed. EBMT retrieves similar examples 
(pairs of source and target expressions), adapting 
them to translate a new source text. 
The feasibility of EBMT has been shown by 
implementing a system which translates Japanese 
noun phrases of the form '~1 no N2" into English 
noun phrases. The result of the experiment was 
encouraging. Bnaed applicability of EBMT was 
shown by studying the d~m from the text corpus. The 
advantages of integrating EBMT with RBMT were 
also discussed. The system has been written in 
Common Lisp, and is running on a Genera 7.2 
Symbolics Lisp Machine at ATR. 
(1) IMPROVEMENT 
The more elaborate the RBMT becomes, the 
less expandable it is. Considerably complex rules 
concerning semantics, context, and the real world, are 
required in machine translation. This is the notorious 
AI bottleneck: not only is it difficult to add a new 
rule to the database of rules that are mutually 
dependent, but it is also difficult to build such a rule 
database itself. Moreover, computation using this 
huge and complex rule database is so slow that it 
forces a developer to abandon efforts to improve the 
system. RBMT is not easily upgraded. 
However, EBMT has no rules, and the use of 
examples is relatively localized. Improvement is 
effected simply by inputting appropriate examples 
into the database. EBMT is easily upgraded, which 
the experiment in section 4.3.2 has shown: the 
more examples we have, the better the 
quality. 
(2) RELIABILITY FACTOR 
One of the main reasons users dislike RBMT 
systems is the so-called "poisoned cookie" problem. 
RBMT has no device to compute the reliability of 
the result. In other words, users of RBMT cannot 
trust any RBMT translation, because it may be 
wrong without any such indication from system. 
Consider the case where all translation processes have 
been completed successfully, yet, the result is 
incorrect. 
In EBMT, a reliability factor is 
assigned to the translation result according 
to the distance between the input and the 
similar examples found \[see the experiment in 
section 4.3.3\]. In addition to this, retrieved examples 
that are similar to the input convince users that the 
translation is accurate. 
(3) TRANSLATION SPEED 
RBMT translates slowly in general because it 
is really a large-scale rule-based system, which 
consists of analysis, transfer, and generation modules 
using syntactic rules, semantic restrictions, structural 
transfer rules, word selections, generation rules, and 
so on. For example, the Mu system has about 2,000 
rewriting and word selection rules for about 70,000 
lexical items (Nagao et al. 1986). 
As recently pointed out (Furuse et al. 1990), 
conventional RBMT systems have been biased 
toward syntactic, semantic, and contextual analysis, 
which consumes considerable computing time. 
However, such deep analysis is not always necessary 
or useful for translation. 
In contrast with this, deep semantic analysis 
is avoided in EBMT because it is assumed that 
translations appropriate for given domain 
can be obtained using domain-specific 
examples (pairs of source and target 
expressions). EBMT directly returns a translation 
without reasoning through a long chain of rules \[see 
191 
sections 2 and 4\]. 
There is fear that retrieval from a large-scale 
example database will prove too slow. However, it 
can be accelerated effectively by both 
indexing (Sumita and Tsutsumi 1988) and 
parallel computing (Sumita and Iida 1991). 
These processes multiply acceleration. Consequently, 
the computation of EBMT is acceptably efficient. 
(4) ROBUSTNESS 
RBMT works on exact-match reasoning. It 
fails to translate when it has no knowledge that 
matches the input exactly. 
However, EBMT works on best-match 
reasoning. It intrinsically translates in a fail-safe way 
\[see sections 2 and 4\]. 
(5) TRANSLATORS EXPERTISE 
Formulating linguistic rules for RBMT is a 
difficult job and requires a linguistically trained staff. 
Moreover, linguistics does not deal with all 
phenomena occurring in real text (Nagao 1988). 
However, examples necessary for EBMT ~Ee 
easy to obtain because a large number of texts and 
their translations are available. These are realization 
of translator expertise, which deals with all real 
phenomena. Moreover, as electronic publishing 
increases, more and more texts will be 
machine-readable (Sadler 1989b). 
EBMT is intrinsically biased toward a 
sublanguage: strictly speaking, toward an example 
database. This is a good feature because it provides a 
way of automatically tuning itself to a 
sublanguage. 

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