Hybrid Approaches to Improvement of Translation Quality 
in Web-based English-Korean Machine Translation 
Sung-Kwon Choi, Han-Min Jung, 
ChuI-Min Sim, Taewan Kim, Dong-In Park 
MT Lab. SERI 
1 Eoun-dong, Yuseong-gu, 
Taejon, 305-333, Korea 
{skchoi, jhm, cmsim, twkim, dipark}@seri.re.kr 
Jun-Sik Park, Key-Sun Choi 
Dept. of Computer Science, KAIST 
373-I Kusong-dong, Yuseong-gu, 
Taejon, 305-701, Korea 
jspark@world.kaist.ac.kr 
kschoi@cs.kaist.ac.kr 
Abstract 
The previous English-Korean MT system 
that was the transfer-based MT system and 
applied to only written text enumerated a 
following brief list of the problems that had 
not seemed to be easy to solve in the near 
future : 1) processing of non-continuous 
idiomatic expressions 2) reduction of too 
many ambiguities in English syntactic 
analysis 3) robust processing for failed or ill- 
formed sentences 4) selecting correct word 
correspondence between several alternatives 
5) generation of Korean sentence style. The 
problems can be considered as factors that 
have influence on the translation quality of 
machine translation system. This paper 
describes the symbolic and statistical hybrid 
approaches to solutions of problems of the 
previous English-to-Korean machine 
translation system in terms of the 
improvement of translation quality. The 
solutions are now successfully applied to the 
web-based English-Korean machine 
translation system "FromTo/EK" which has 
been developed from 1997. 
Introduction 
The transfer-based English-to-Korean machine 
translation system "MATES/EK" that has been 
developed from 1988 to 1992 in KAIST(Korean 
Advanced Institute of Science and Technology) 
and SERl(Systems Engineering Research 
Institute) enumerated following list that doesn't 
seem to be easy to solve in the near future in 
terms of the problems for evolution of the 
system (Choi et. al., 1994) : 
• processing of non-continuous idiomatic 
expressions 
• generation of Korean sentence style 
• reduction or ranking of too many 
ambiguities in English syntactic analysis 
• robust processing for failed or ill-formed 
sentences 
• selecting correct word correspondency 
between several alternatives 
The problems result in dropping a translation 
assessment such as fidelity, intelligibility, and 
style (Hutchins and Somers, 1992). They can be 
the problems with which MATES/EK as well as 
other MT systems have faced. 
This paper describes the symbolic and statistical 
hybird approaches to solve the problems and to 
improve the translation quality of web-based 
English-to-Korean machine translation. 
1 System Overview 
English-to-Korean machine translation system 
"FromTo/EK" has been developed from 1997, 
solving the problems of its predecessor 
"MATES/EK" and expanding its coverage to 
WWW. FromTo/EK has basically the same 
formalism as MATES/EK that does English 
sentence analysis, transforms the result (parse 
251 
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Interface 
Translation Engine 
Eng 
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x., I K.,~,. ~ 
,,r,,r t-~ ~'nn','lrx~rPr ' 
Knowledge and Dictionary User Interface 
Figure 1: The System Configuration of FromTo/EK 
tree) into an intermediate representation, and 
then transforms it into a Korean syntactic 
structure to construct a Korean sentence. Figure 
1 shows the overall configuration of FromTo/EK. 
FromTo/EK consists of user interface for 
English and Korean, translation engine, and 
knowledge and dictionaries. The black boxes in 
the Figure 1 mean the modules that have existed 
in MATES/EK, while the white ones are the new 
modules that have been developed to improve 
the translation quality. Next chapters describe 
the new modules in detail. 
2 Domain Recognizer and Korean 
sentence style 
In order to identify the domain of text and 
connect it to English terminology lexicon and 
Korean sentence style in Korean generation, we 
have developed a domain recognizer. 
We adapted a semi-automated decision tree 
induction using C4.5 (Quinlan, 1993) among 
diverse approaches to text categorization such as 
decision tree induction (Lewis et. al., 1994) and 
neural networks (Ng et. aL, 1997), because a 
semi-automated approach showed perhaps the 
best performance in domain identification 
according to (Ng et. al., 1997). Twenty-five 
domains were manually chosen from the 
categories of awarded Web sites. We collected 
0.4 million Web pages by using Web search 
robot and counted the frequency of words to 
extract features for domain recognition. The 
words that appeared more than 200 times were 
used as features. Besides we added some 
manually chosen words to features because the 
features extracted automatically were not able to 
show the high accuracy. 
Given an input text, our domain recognizer 
assigns one or more domains to an input text. 
The domains can raise the translation quality by 
activating the corresponding domain-specific 
terminology and selecting the correct Korean 
sentence style. For example, given a "driver", it 
may be screw driver, taxi driver or device driver 
program. After domain recognizer determines 
each domain of input text, "driver" can be 
translated into its appropriate Korean equivalent. 
The domain selected by the domain recognizer is 
able to have a contribution to generate a better 
Korean sentence style because Korean sentence 
style can be represented in various ways by the 
verbal endings relevant to the domain. For 
example, the formal domains such as technology 
252 
and law etc. make use of the plain verbal ending 
like 'ta' because they have carateristics of 
formality, while the informal domains such as 
weather, food and fashion etc. are related to the 
polite verbal ending 'supnita' because they have 
carateristics of politeness. 
3 Compound Unit Recognition 
parsing mechanism. Partial parser operates on 
cyclic trie and simple CFG rules for the fast 
syntactic constraint check. The experimental 
result showed our syntactic verification 
increased the precision of CU recognition to 
99.69%. 
4 Competitive Learning Grammar 
One of the problems of rule-based translation 
has been the idiomatic expression which has 
been dealt mainly with syntactic grammar rules 
(Katoh and Aizawa, 1995) "Mary keeps up with 
her brilliant classmates." and "I prevent him 
from going there." are simple examples of 
uninterupted and interupted idiomatic 
expressions expectively. 
In order to solve idiomatic expressions as well as 
collocations and frozen compound nouns, we 
have developed the compound unit(CU) 
recognizer (Jung et. al., 1997). It is a plug-in 
model locating between morphological and 
syntactic analyzer. Figure 2 shows the structure 
of CU recognizer. 
English ------~. Morphological Analyzer ~ , 
~ S)'atac " ' 
.... CFG Grammar ,~ 
Figure 2 : System structure of CU recognizer 
The recognizer searches all possible CUs in the 
input sentence using co-occurrence constraint 
string/POS and syntactic constraint and makes 
the CU index. Syntactic verifier checks the 
syntactic verification of variable constituents in 
CU. For syntactic verifier we use a partial 
For the parse tree ranking of too many 
ambiguities in English syntactic analysis, we use 
the mechanism to insert the competitive 
probabilistics into the rules. To decide the 
correct parse tree ranking, we compare two 
partial parse trees on the same node level with 
competitive relation and add ct (currently, 0.01) 
to the better one, but subtract ct from the worse 
one on the base of the intuition of linguists. This 
results now in raising the better parse tree higher 
in the ranking list of the parse trees than the 
worse one. 
5 Robust Translation 
In order to deal with long sentences, parsing- 
failed or ill-formed sentences, we activate the 
robust translation. It consists of two steps: first, 
long sentence segmentation and then fail 
softening. 
5.1 Long Sentence Segmentation 
The grammar rules have generally a weak point 
to cover long sentences. If there are no grammar 
rules to process a long sentence, the whole parse 
tree of a sentence can not be produced. Long 
sentence segmentation produces simple 
from long sentences before parsing fragements 
fails. 
We use the 
clue of the 
sentence 
POS sequence of input sentence as a 
segmentation. If the length of input 
exceeds pre-defined threshold, 
currently 21 for segmentation level I and 25 for 
level II, the sentence is divided into two or more 
parts. Each POS trigram is separately applied to 
the level 1 or II. After segmenting, each part of 
253 
input sentence is analyzed and translated. The 
following example shows an extremely long 
sentence (45 words) and its long sentence 
segmentation result. 
\[Input sentence\] 
"Were we to assemble a Valkyrie to challenge 
IBM, we could play Deep Blue in as many 
games as IBM wanted us to in a single match, in 
fact, we could even play multiple games at the 
same time. Now - - wouldn't that be 
interesting?" 
\[Long Sentence Segmentation\] 
"Were we to assemble a Valkyrie to challenge 
IBM, / (noun PUNCT pron) we could play Deep 
Blue in as many games as IBM wanted us to in a 
single match, / (noun PUNCT adv) in fact, / 
(noun PUNCT pron) we could even play 
multiple games at the same time, / (adv PUNCT 
adv) Now - - / (PUNCT PUNCT aux) wouldn't 
that be interesting?" 
5.2 Fail Softening 
For robust translation we have a module 'fail 
softening' that processes the failed parse trees in 
case of parsing failure. Fail softening finds set of 
edges that covers a whole input sentence and 
makes a parse tree using a virtual sentence tag. 
We use left-to-right and right-to-left scanning 
with "longer-edge-first" policy. In case that there 
is no a set of edges for input sentence in a 
scanning, the other scanning is preferred. If both 
make a set of edges respectively, "smaller-set- 
first" policy is applied to select a preferred set, 
that is, the number of edges in one set should be 
smaller than that of the other (e.g. if n(LR)=6 
and n(RL)=5, then n(RL) is selected as the first 
ranked parse tree, where n(LR) is the number of 
left-to-right scanned edges, and n(RL) is the 
number of right-to-left scanned edges). We use a 
virtual sentence tag to connect the selected set of 
edges. One of our future works is to have a 
mechanism to give a weight into each edge by 
syntactic preference. 
6 Large Collocation Dictionary 
We select a correct word equivalent by using 
lexical semantic marker as information 
constraint and large collocation dictionary in the 
transfer phase. 
The lexical semantic marker is applied to the 
terminal node for the relational representation, 
while the collocation information is applied to 
the non-terminal node. 
The large collocation dictionary has been 
collected from two resources; EDR dictionary 
and Web documents. 
7 Test and Evaluation 
A semi-automated decision tree of our domain 
recognizer uses as a feature twenty to sixty 
keywords which are representative words 
extracted from twenty-five domains. To raise the 
accuracy of the domain identifier, manually 
chosen words has been also added as features. 
For learning of the domain identifier, each 
thousand sentence from twenty-five domains is 
used as training sets. We tested 250 sentences 
that are the summation of each ten sentences 
extracted from twenty-five domains. These test 
sentences were not part of training sets. The 
domain identifier outputs two top domains as its 
result. The accuracy of first top domain shows 
45% for 113 sentences. When second top 
domains are applied, the accuracy rises up to 
75%. 
In FromTo/EK, the analysis dictionary consists 
of about 70,000 English words, 15,000 English 
compound units, 80,000 English-Korean 
bilingual words, and 50,000 bilingual 
collocations. The domain dictionary has 5,000 
words for computer science that were extracted 
from IEEE reports. 
In order to make the evaluation as objective as 
possible we compared FromTo/EK with 
MATES/EK on 1,708 sentences in the IEEE 
computer magazine September 1991 issue, 
which MATES/EK had tested in 1994 and 
254 
whose length had been less than 26 words. Table 
1 shows the evaluation criteria. 
Table 1 : The evaluation criteria 
Degree Meaning 
4 The meaning of the sentence is 
(Perfect) perfectly clear. 
3 (Good) 
2 (OK) 
The meaning of the sentence is 
almost clear. 
The meaning of the sentence can be 
understood after several readings. 
1 (Poor) The meaning of the sentence can be 
guessed only after a lot of readings. 
0(Fail) The meaning of the sentence 
cannot be guessed at all. 
With the evaluation criteria three master degree 
students whom we randomly selected compared 
and evaluated the translation results of 1,708 
sentences of MATES/EK and those of 
FromTo/EK. We have considered the degrees 4, 
3, and 2 in the table 1 as successful translation 
results. Figure 3 shows the evaluation result. 
tSumh~ d mo~,~lb. 
Inmlll~l m) 
100 
0 ~ 
4 5 6 7 8 9 lO 11 12 13 14 15 16 1"7 18 19 20 21 ~' 23 24 
Figure 3 : The evaluation of 1,708 sentences 
Figure 3 shows a translation quality of both 
FromTo/EK and MATES/EK according to the 
length of a sentence. More than 84% of 
sentences that FromTo/EK has translated is 
understood by human being. 
8 Conclusion 
In this paper we described the hybrid approaches 
to resolution of various problems that 
MATES/EK as the predecessor of FromTo/Ek 
had to overcome. The approaches result in 
improving the translation quality of web-based 
documents. 
FromTo/EK is still under growing, aiming at the 
better Web-based machine translation, and 
scaling up the dictionaries and the grammatical 
coverage to get the better translation quality. 

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