A PROPER TREATMEMT OF SYNTAX AND SEMANTICS IN MACHINE TRANSLATION 
¥oshihiko Nitta, Atsushi Okajima, Hiroyuki Kaji, 
Youichi Hidano, Koichiro Ishihara 
Systems Development Laboratory, Hitachi, Ltd. 
1099 Ohzenji Asao-ku, Kawasaki-shi, 215 JAPAN 
ABSTRACT 
A proper treatment of syntax and semantics in 
machine translation is introduced and discussed 
from the empirical viewpoint. For English- 
Japanese machine translation, the syntax directed 
approach is effective where the Heuristic Parsing 
Model (HPM) and the Syntactic Role System play 
important roles. For Japanese-English 
translation, the semantics directed approach is 
powerful where the Conceptual Dependency Diagram 
(CDD) and the Augmented Case Marker System (which 
is a kind of Semantic Role System) play essential 
roles. Some examples of the difference between 
Japanese sentence structure and English sentence 
structure, which is vital to machine translation~ 
are also discussed together with various 
interesting ambiguities. 
I INTRODUCTION 
We have been studying machine translation 
between Japanese and English for several years. 
Experiences gained in systems development and in 
linguistic data investigation suggest that the 
essential point in constructing a practical 
machine translation system is in the appropriate 
blending of syntax directed processing and the 
semantics directed processing. 
In order to clarify the above-mentioned 
suggestion, let us compare the characteristics of 
the syntax directed approach with those of the 
semantics directed approach. 
The advantages of the syntax directed approach 
are as follows: 
(i) It is not so difficult to construct the 
necessary linguistic data for syntax directed 
processors because the majority of these data can 
be reconstructed from already established and 
well-structured lexical items such as verb pattern 
codes and parts of speech codes, which are 
overflowingly abundant in popular lexicons. 
(2) The total number of grammatical rules 
necessary for syntactic processing usually stays 
within a controllable range. 
(3) The essential aspects of syntactic 
processing are already well-known, apart from 
efficiency problems. 
The disadvantage of the syntax directed 
approach is its insufficient ability to resolve 
various ambiguities inherent in natural languages. 
On the other hand, the advantages of the 
semantics directed approach are as follows: 
(i) The meaning of sentences or texts can be 
grasped in a unified form without being affected 
by the syntactic variety. 
(2) Semantic representation can play a pivotal 
role for language transformation and can provide 
a basis for constructing a transparent machine 
translation system, because semantic representa- 
tion is fairly independent of the differences in 
language classes. 
(3) Consequently, semantics directed internal 
representation can produce accurate translations. 
The disadvantages of the semantics directed 
approach are as follows: 
(I) It is not easy to construct a semantic 
lexicon which covers real world phenomena of a 
reasonably wide range. The main reason for this 
difficulty is that a well-established and 
widely-accepted method of describing semantics 
does not exist. (For strongly restricted 
statements or topics, of course, there exist 
well-elaborated methods such as Montague grammar 
\[2\], Script and MOP (Memory Organization Packet) 
theory \[13\], Procedural Semantics \[14\], and 
Semantic Interlingual Representation \[15\].) 
(2) The second but intractable problem is that, 
even if you could devise a fairly acceptable 
method to describe semantics, the total number of 
semantic rule descriptions may expand beyond all 
manageable limits. 
Therefore, we think that it is necessary to 
seek proper combinations of syntactic processing 
and semantic processing so as to compensate for 
the disadvantages of each. 
The purpose of this paper is to propose a 
proper treatment of syntax and semantics in 
machine translation systems from a heuristic 
viewpoint, together with persuasive examples 
obtained through operating experiences. A 
sub-language approach which would put some 
moderate restrictions on the syntax and semantics 
of source language is also discussed. 
159 
II SYNTAX AND SEMANTICS 
It is not entirely possible to distinguish a 
syntax directed approach from a semantics 
directed approach, because syntax and semantics 
are always performing their linguistic functions 
reciprocally• 
As Wilks \[16\] points out, it is plausible but a 
great mistake to identify syntactic processing 
with superficial processing, or to identify 
semantic processing with deep processing. The 
term "superficial" or "deep" only reflects the 
intuitive distance from the language represen- 
tation in (superficial) character strings or from 
the language representation in our (deep) minds. 
Needless to say, machine translation inevitably 
has something to do with superficial processing• 
In various aspects of natural language 
processing, it is quite common to segment a 
superficial sentence into a collection of phrases• 
A phrase itself is a collection of words• In 
order to restructure the collection of phrases, 
the processor must first of all attach some sorts 
of labels to the phrases• If these labels are 
something like subject, object, complement, etc., 
then we will call this processor a syntax directed 
processor, and if these labels are something like 
agent, object, instrument, etc., or animate, 
inanimate, concrete, abstract, human, etc., then 
we will call this processor a semantics directed 
processor• 
The above definition is oversimplified and of 
course incomplete, but it is still enough for the 
arguments in this paper• 
III SYNTAX DIRECTED APPROACH: 
A PROTOTYPE ENGLISH-JAPANESE 
MACHINE TRANSLATION SYSTEM 
So far we have developed two prototype machine 
translation systems; one is for English-Japanese 
translation \[6\] and the other is for Japanese- 
English translation• 
The prototype model system for English- 
Japanese translation (Figure I) is constructed as 
a syntax directed processor using a phrase 
structure type internal representation called HPM 
(Heuristic Parsing Model), where the semantics is 
utilized to disambiguate dependency relationships• 
The somewhat new name HPM (Heuristic Parsing 
Model) reflects the parsing strategy by which the 
machine translation tries to simultate the 
heuristic way of actual human of language 
translation• The essential features of heuristic 
translation are summarized in the following three 
steps: 
(I) To segment an input sentence into phrasal 
elements (PE) and clausal elements (CE). 
(2) To assign syntactic roles to PE's and CE's, 
and restructure the segmented elements into 
tree-forms by governing relation, and into 
link-forms by modifying relation• 
(3) To permute the segmented elements, and to 
assign appropriate Japanese equivalents with 
necessary case suffixes and postpositions. 
Noteworthy findings from operational 
experience and efforts to improve the prototype 
model are as follows: 
Lexicons \[7\] 
entry: 
• word 
• phrase 
• idiom 
• etc. I 
description: 
• attribute 
• Japanese equivalent 
• controlling marks 
for analysis, 
transformation and 
generation 
• etc. 
Input English Sentence 
I Lexicon Retrieval I_ _~'~'----"---'~ 
I Morphological Analysis - llnternal Language ' IRepresentation 
O on HPM\] ~Syntactic Analysis 
\[based on HPM\] 
Tree/Link Transformation 
\[Sentence Generation 
~Morphological Synthesis 
=I F•adj ustment of tense and l 
| mode | i !\[Parsed ~|•assignment of | Tree/Link \[ L postpositions 
J - 
G 
Post-editing Support I_ 
~ \['solution to manifold\] 
\[meanings J 1 ~.. 
G. 
Output Japanese Sentence 
Figure 1 Configuration of Machine Translation System: ATHENE \[6\] 
160 
TWith .... helpTf ........... Tj~the Jap ..... Tare beglnningTa 10-year R&D effortTintendedTto yield~a fifth g ..... tion systemT.~ \ \ 
\ \ I I \ \ \ \ \ I I / / /// / // 
• WE: Word Element 
•PE; Phrasal Element ' 
CP: Clausal Element • 
SE: Sentence 
• This sample English sentence is taken from Datamation Jan. 1982. 
Figure 2 An Example of Phrase Structure Type Representation 
(I) The essential structure of English sentences 
should be grasped by phrase structure type 
representations. 
An example of phrase strucure type 
representation, which we call HPM (Heuristic 
Parsing Model), is illustrated in Figure 2. In 
Figure 2, a parsed tree is composed of two 
substructures. One is "tree ( ~/ )," 
representing a compulsory dependency relation, 
and the other is "link (k~)," representing an 
optional dependency relation. Each node 
corresponds to a certain constituent of the 
sentence. 
The most important constituent is a "phrasal 
element (PE)" which is composed of one or more 
word element(s) and carries a part of the 
sentential meaning in the smallest possible 
form. PE's are mutually exclusive. In Figure 2, 
PE's are shown by using the "segmenting marker 
(T)", such as 
TWith some help (ADVL)\[, 
\[from overseas (ADJV)\[j T,(co~)T, 
Tthe Japanese (SUBJ)T 
and 
Tare beginning (GOV)T, 
where the terminologies in parentheses are the 
syntactic roles which will be discussed later. 
A "clausal element (CE)" is composed of one or 
more PE('s) which carries a part of sentential 
meaning in a nexus-like form. A CE roughly 
corresponds to a Japanese simple sentence such 
as: "%{wa/ga/wo/no/ni} ~ {suru/dearu} \[koto\]." 
CE's allow mutual intersection. Typical examples 
are the underlined parts in the following: 
"It is important for you to do so." 
"... intended to yield a fifth generation system." 
One interesting example in Figure 2 may be the 
part 
"With some help from overseas", 
which is treated as only two consecutive phrasal 
elements. This is the typical result of a syntax 
directed parser. In the case of a semantics 
directed parser, the above-mentioned part will be 
treated as a clausal element. This is because 
the meaning of this part is "(by) getting some 
help from overseas" or the like, which is rather 
clausal than phrasal. 
(2) Syntax directed processors are effective and 
powerful to get phrase structure type parsed 
trees. 
Our HPM parser operates both in a top-down way 
globally and in a bottom-up way locally. An 
example of top-down operation would be the 
segmentation of an input sentence (i.e. the 
sequence of word elements (WE's)) to get phrasal 
elements (PE), and an example of bottom-up 
operation would be the construction of tree-forms 
or link-forms to get clausal elements (CE) or a 
sentence (SE). These operations are supported by 
syntax directed grammatical data such as 
verb dependency type codes (cf. Table i, which is 
a simplified version of Hornby's classification 
\[5\]), syntactic role codes (Table 2) and some 
production rule type grammars (Table 3 & Table 
4). It may be permissible to say that all these 
syntactic data are fairly compact and the kernel 
parts are already well-elaborated (cf. \[i\], \[8\], 
\[ii\], \[12\]). 
161 
Code 
Vl 
V2 
V3 
V6 
V7 
V8 
V14 
Code 
SUBJ 
OK/ 
TOOBJ 
NAPP 
GOV 
TOGOV 
ENGOV 
ADJV 
ENADj 
ADVL 
SENT 
Table 1 Dependency Pattern of Verb 
Verb Pattern 
Be + ... 
Vi (# Be) + Complement, 
It/There + Vi + ... 
Vi \[+ Adverbial Modifier\] 
Vt + To-infinitive 
Vt + Object 
vt + that + ... 
Vt + Object \[+not\] + 
To-infinitive 
Examples 
be 
get, look 
rise~ walk 
intend 
begin~ yield 
agree, think 
know, bring 
Table 2 Syntactic Roles 
Role 
Subject 
Object 
Object in To-infinitive Form 
Noun in Apposition 
Governing Verb 
Governing Verb in To-infinitive Form 
Governing Verb in Past Participle Form 
Adjectival 
Adjectival in Past Participle Form 
Adverbial 
Sentence 
(3) The weak point of syntax directed processors 
is their insufficient ability to disambiguate; 
i.e. the ability to identify dependency types of 
verb phrases and the ability to determine heads 
of prepositional phrase modifiers. 
(4) In order to boost the aforementioned 
disambiguation power, it is useful to apply 
semantic filters that facilitate the selective 
restrictions on linking a verb with nominals and 
on linking a modifier with its head. 
A typical example of the semantic filter is 
illustrated in Figure 3. The semantic filter may 
operate along with selective restriction rules 
such as: 
• N22 (Animal) + with + N753 (Accessory) 
Plausible 
\[': N22 is equipped with N753\] 
• V21 (Watching-Action) + with + N541 
(Watching Instrument) ~ OK 
\[vV21 by using N541 as an instrument\] 
The semantic filter is not complete, 
especially for metaphorical expressions. A bird 
could also use binoculars. 
Table 3 Rules for Assigning Syntactic Roles to Phrasal Elements 
Pattern to be Scanned New Pattern to be Generated 
TOGOV~ + OBJ 
*: focus, --: not mentioned, ~: empty, \[...\]: optional 
Table 4 Rules for Constructing Clausal Elements 
Pattern to be Scanned New Element to be Generated 
I* \[ SENT | 
162 
He saw a bird with a ribbon. 
He saw a bird with binoculars• 
O I II 
f> 
(a) and (d) are plausible. 
* X~_.. Y implies that X Js modified by Y. 
Figure 3 A Typical Operation of Semantic Filter 
(5) The aforementioned semantic filters are 
compatible with syntax directed processors; i.e. 
there is no need to reconstruct processors or to 
modify internal representations. It is only 
necessary to add filtrating programs to the 
syntax directed processor. 
One noteworthy point is that the thesaurus for 
controlling the semantic fields or semantic 
features of words should be constructed in an 
appropriate form (such as word hierarchy) so as 
to avoid the so-called combinatorial explosion of 
the number of selective restriction rules. 
(6) For the Japaneses sentence generating 
process, it may be necessary to devise a very 
complicated semantic processor if a system to 
produce natural idiomatic Japanese sentences is 
desired. But the majority of Japanese users may 
tolerate awkward word-by-word translation and 
understand its meaning. Thus we have concluded 
that our research efforts should give priority to 
the syntax directed analysis of English 
sentences. The semantics directed generation of 
Japanese sentences might not be an urgent issue; 
rather it should be treated as a kind of profound 
basic science to be studied without haste. 
(7) Even though the output Japanese translation 
may be an awkward word-by-word translation, it 
should be composed of pertinent function words 
and proper equivalents for content words. 
Otherwise it could not express the proper meaning 
of the input English sentences. 
(8) In order to select proper equivalents, 
semantic filters can be applied fairly 
effectively to test the agreement among the 
semantic codes assigned to words (or phrases). 
Again the semantic filter is not always 
complete. For example, in Figure 2, the verb 
"yield" has at least two different meanings (and 
consequently has at least two different Japanese 
equivalents): 
"yield"-->I"produce" (ffi Umidasu) 
\["concede" (ffi Yuzuru). 
But it is neither easy nor certain how to 
devise a filter to distinguish the above two 
meanings mechanically. Thus we need some human 
aids such as post-editing and inter-editing. 
(9) As for the pertinent selection of function 
words such as postpositions, there are no formal 
computational rules to perform it. So we must 
find and store heuristic rules empirically and 
then make proper use of them. 
Some heruistic rules to select appropriate 
Japanese postpositions are shown in Table 5. 
Table 5 Heuristic Rules for Selecting 
Postpositions for "in + N" 
Semantic Japanese Post- 
positions for Category of N ADVL/ADJV 
in+Nl (NlfPlace) Nl+de/Nl+niokeru 
in+N3 (N3=Time) N3+ni/N3+no 
in+N3&N4 --/N3&Nd+go-ni 
(Nd=Quantit~) 
in+N6 N6÷dewa/N6+no 
(N6fAbstract 
Concept) 
in+N8 (N8ffiMeans) NS+de/NS+niyoru 
• No rules. +de/+no 
• A kind of +wo-kite/ 
idiom \[7\] to +wo-kita 
be retrieved +wo-kakete/ 
directly from +wo-kaketa 
a lexicon. 
English Examples 
in California 
in Spring 
in two days 
in my opinion 
in Z-method 
(speak) in English 
in uniform 
in spectacles 
(i0) To get back to the previous findings (I) 
and (2), the heuristic approach was also found to 
be effective in segmenting the input English 
sentence into a sequence of phrasal elements, and 
in structuring them into a tree-llke dependency 
diagram (cf. Figure 2). 
(Ii) A practical machine translation should be 
considered from a kind of heuristic viewpoint 
rather than from a purely rigid analytical 
linguistic viewpoint. One persuasive reason for 
this is the fact that humans, even foreign 
language learners, can translate fairly difficult 
English sentences without going into the details 
of parsing problems. 
IV SEMANTICS DIRECTED APPROACH: 
A PROTOTYPE JAPANESE-ENGLISH 
MACHINE TRANSLATION SYSTEM 
The prototype model system for Japanese- 
English translation is constructed as a semantics 
directed processor using a conceptual dependency 
diagram as the internal representation. 
Noteworthy findings through operational 
experience and efforts to improve on the 
prototype model are as follows: 
163 
(I) Considering some of the characteristics of 
the Japanese language, such as flexible word 
ordering and ambiguous usage of function words, 
it is not advantageous to adopt a syntax directed 
representation for the internal base of language 
transformation. 
For example, the following five Japanese 
sentences have almost the same meaning except for 
word ordering and a subtle nuance. Lowercase 
letters represent function words. 
Boku wa Fude de Tegami wo Kaku. 
(11 (brush)(with)(letter) (write) 
Boku wa tegami wo Fude de Kaku. 
Fude de Boku wa Tegami wo Kaku. 
Tegami wa Boku wa Fude de Kaku. 
Boku wa Tegami wa Fude de Kaku. 
(2) Therefore we have decided to adopt the 
conceptual dependency diagram (CDD) as a compact 
and powerful semantics directed internal 
representation. 
Our idea of the CDD is similar to the 
well-known dependency grammar defined by Hays 
\[4\] and Robinson \[9\] \[i0\], except for the 
augmented case markers which play essentially 
semantic roles. 
(31 The conceptual dependency diagram for 
Japanese sentences is composed of predicate 
phrase nodes (PPNs in abbreviationl and nominal 
phrase nodes (NTNs in abbreviation). Each PPN 
governs a few NPNs as its dependants. Even among 
PPNs there exist some governor-dependant 
relationships. 
Examples of formal CDD description are: 
PPN (NPNI, NPN2, ... N-PNnl, 
Kaku (Boku, Te~ami, Fude), 
Write (I, Letter, Brus--~'~, 
where the underlined word "~' represents the m 
concept code corresponding to the superficial 
word "a", and the augmented case markers are 
omitted. 
In the avove description, the order of 
dependants NI, N2, ..., Nn are to be neglected. 
For example, 
PPN (NPNn, ..., NPN2, NPNI) 
is identical to the above first formula. This 
convention may be different from the one defined 
by Hays \[4\]. Our convention was introduced to 
cope with the above-mentioned flexible word 
ordering in Japanese sentences. 
(4) The aforementioned dependency relationships 
can be represented as a linking topology, where 
each link has one governor node and one dependant 
node as its top and bottom terminal point (Figure 
4). 
(5) The links are labeled with case markers. 
Our case marker system is obtained by augmenting 
the traditional case markers such as Fillmore's 
\[3\] from the standpoint of machine translation. 
For the PPN-NPN link, its label usually 
represents agent, object, goal, location, topic, 
etc. For the PPN-PPN link, its label is usually 
represent causality, temporality, 
restrictiveness, etc. (cf. Figure 4). 
PPN' 
PPN ~'C4 --~ Kaku Write __ -~.J /T0\ /T0  
NPN I NPN 2 NPN 3 8oku Tegaml Fude I Letter Brush 
* CI: case markar 
Figure 4 Examples of a Conceptual Dependency 
Diagram (CDD) 
(6) As for the total number of case markers, our 
current conclusion is that the number of 
compulsory case markers to represent predicative 
dominance should be small, say around 20; and 
that the number of optional case markers to 
represent adjective or adverbial modification 
should be large, say from 50 to 70 (Table 6). 
(7) The reason for the large number of optional 
case markers is that the detailed classification 
of optional cases is very useful for making an 
appropriate selection of prepositions and 
participles (Table 7). 
(g) Each NPN is to be labeled with some properly 
selected semantic features which are under the 
control of a thesaurus type lexicon. Semantic 
features are effective to disambiguate 
predicative dependency so as to produce an 
appropriate English verb phrase. 
(9) The essential difference between a Japanese 
sentence and the equivalent English sentence can 
be grasped as the difference in the mode of PPN 
selections, taken from the viewpoint of 
conceptual dependency diagram (Figure 51. Once 
an appropriate PPN selection is made, it will be 
rather simple and mechanical to determine the 
rest of the dependency topology. 
(I0) Thus the essential task of Japanese-English 
translation can be reduced to the task of 
constructing the rules for transforming the 
dependency topology by changing PPNs, while 
preserving the meaning of the original dependency 
topology (cf. Figure 5). 
(Ill All the aforementioned findings have 
something to do with the semantic directed 
approach. Once the English oriented conceptual 
dependency diagram is obtained, the rest of the 
translation process is rather syntactic. That 
is, the phrase structure generation can easily be 
handled with somewhat traditional syntax directed 
processors. 
164 
(12) As is well known, the Japanese language has 
a very high degree of complexity and ambiguity 
mainly caused by frequent ellipsis and functional 
multiplicity, which creates serious obstacles for 
the achievement of a totally automatic treatment 
of "raw" Japanese sentences. 
(ex i) "Sakana wa Taberu." 
(fish) (eat) 
has at least two different interpretations: 
• "\[Sombody\] can eat a fish." 
. "The fish may eat \[something\]." 
Table 6 Case Markers for CDD (subset only) 
Predicative A Agent 
Dominance 0 Object 
(Compulsory) C Complement 
R Recipient 
AC Agent in Causative 
T Theme, Topic (Mental Subject) 
P Partner 
Q Quote 
RI Range of Interest 
RQ Range of Qualification 
RM Range of Mention 
I Instrument 
E Element 
Adverbial CT Goal in Abstract Collection 
Modification CF Source in Abstract Collection 
(Optional) TP Point in Time 
Adjective ET Embedding Sentence Type Modifier 
Modification whose gapping is Theme 
(Optional) EA whose gapping is Agent 
EO whose gapping is Object 
Link and ~" ilnking through "AND" 
Conjunction BT Conjunction through "BUT" 
(Optional) ......... 
(ex 2) "Kawaii Ningyou wo Motteiru Onnanoko." 
(lovely) (doll) (carry) (girl) 
has also two different interpretations: 
"The lovel~ ~irl who carries a doll with 
her." 
"The girl who carries a lovel\[ doll with 
her." 
(13) Thus we have judged that some sub-Japanese 
language should be constructed so as to restrict 
the input Japanese sentences within a range of 
clear tractable structures. The essential 
restrictions given by the sub-language should be 
concerned with the usage of function words and 
sentential embeddings. 
Table 7 Detailed Classification of Optional Case 
Markers for Modification (subset only) 
Phase Code Most-Likely Prepositions or Participles 
F 
T 
D 
P 
I 
O 
V 
U 
S 
B 
A 
AL 
H 
AB 
SE 
WI 
•.. 
from 
to, till 
during 
at 
in, inside 
out, outside 
over, above 
under, below 
beside 
before, in front of 
after, behind 
along 
through 
over, superior to 
apart from 
within 
. Case Marker E Body Code + Phase Code 
• Body Code ~ T (=Time)IS (=Space)IC (=Collection) 
• Kasoukioku-~usesu-Hou nlyorl, Dalyouryou-Deitasetto 
eno Kourltsu no Yol Nyushutsuryoku ga Kanou nl Naru. ~ 
Analysls 
~ 4)' 
J i 
\] II i l  oon I 
,Ival.o r °°IUf7 ~itasetto I IT J 
". ...... ~ /~ A 5)" 
Naru (-Become)-type CDD 
Transformation > 
" The virtual storage access method enables the efficient 
input-output processing to a large capacity data set. 
~ Generatlon 
4) 
I enable I 
access method processing 
/ 3) \ 5) 
Suru (=Make)-type CDD 
Figure 5 Difference between Japanese and English Grasped Through CDD 
165 
(IA) A sub-language approach will not fetter the 
users, if a Japanese-Engllsh translation system 
is used as an English sentence composing aid for 
Japanese people. 
V CONCLUSION 
We have found that there are some proper 
approaches to the treatment of syntax and 
semantics from the viewpoint of machine 
translation. Our conclusions are as follows: 
(i) In order to construct a practical 
English-Japanese machine translation system, it 
is advantageous to take the syntax directed 
approach, in which a syntactic role system plays 
a central role, together with phrase structure 
type internal representation (which we call HPM). 
(2) In English-Japanese machine translation, 
syntax should be treated in a heuristic manner 
based on actual human translation methods. 
Semantics plays an assistant role in 
disambiguating the dependency among phrases. 
(3) In English-Japanese machine translation, an 
output Japanese sentence can be obtained directly 
from the internal phrase structure representation 
(HPM) which is essentially a structured set of 
syntactic roles. Output sentences from the above 
are, of course, a kind of literal translation of 
stilted style, but no doubt they are 
understandable enough for practical use. 
(4) In order to construct a practical 
Japanese-English machine translation system, it 
is advantageous to take the approach in which 
semantics plays a central role together with 
conceptual dependency type internal 
representation (which we call CDD). 
(5) In Japanese-English machine translation, 
augmented case markers play a powerful semantic 
ro le. 
(6) In Japanese-English machine translation, the 
essential part of language transformation between 
Japanese and English can be performed in terms of 
changing dependency diagrams (CDD) which involves 
predicate replacements. 
One further problem concerns establishing a 
practical method of compensating a machine 
translation system for its mistakes or 
limitations caused by the intractable 
complexities inherent to natural languages. This 
problem may be solved through the concept of 
sublanguage, pre-editing and post-editing to 
modify source/target languages. The sub-Japanese 
language approach in particular seems to be 
effective for Japanese-English machine 
translaton. One of our current interests is in a 
proper treatment of syntax and semantics in the 
sublanguage approach. 
ACKNOWLEDGEMENTS 
We would like to thank Prof. M. Nagao of Kyoto 
University and Prof. H. Tanaka of Tokyo Institute 
of Technology, for their kind and stimulative 
discussion on various aspects of machine 
translation. Thanks are also due to Dr. J. 
Kawasaki, Dr. T. Mitsumaki and Dr. S. Mitsumori 
of 5DL Hitachi Ltd. for their constant 
encouragement to this work, and Mr. F. Yamano and 
Mr. A. Hirai for their enthusiastic assistance in 
programming. 
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166 
