JETR: A ROBUST MACHINE TRANSLATION SYSTEM 
Rika Yoshii 
Department of Information and Computer Science 
University of California, Irvine, 
Irvine, California, 92717 t 
ABSTRACT 
This paper presents an expectation-based Japanese- 
to-English translation system called JETR which relies 
on the forward expectation-refinement process to 
handle ungrammatical sentences in an elegant and 
efficient manner without relying on the presence of 
particles and verbs in the source text. JETR uses a 
chain of result states to perform context analysis for 
resolving pronoun and object references and filling 
ellipses. Unlike other knowledge-based systems, JETR 
attempts to achieve semantic, pragmatic, structural and 
lexical invariance. 
INTRODUCTION 
Recently there has been a revitalized interest in 
machine translation as both a practical engineering 
problem and a tool to test various Artificial 
Intelligence (AI) theories. As a result of increased 
international communication, there exists today a 
massive Japanese effort in machine translation. 
However, systems ready for commercialization are still 
concentrating on syntactic information and are unable 
to translate syntactically obscure but meaningful 
sentences. Moreover, many of these systems do not 
perform context analysis and thus cannot fill ellipses 
or resolve pronoun references. Knowledge-based 
systems, on the other hand, tend to discard the syntax 
of the source text and thus are unable to preserve the 
syntactic style of the source text. Moreover, these 
systems concentrate on understanding and thus do not 
preserve the semantic content of the source text. 
An expectation-based approach to "Japanese-to- 
English machine translation is presented. The 
approach is demonstrated by the JETR system which is 
designed to translate recipes and instruction booklets. 
Unlike other Japanese-to-English translation systems, 
which rely on the presence of particles and main verbs 
in the source text (AAT 1984, Ibuki 1983, Nitta 1982, 
tThe author is now located at: 
Rockwell International Corp. 
Autonetics Strategic Systems Division 
Mail Code: GA42 
3370 Miraloma Avenue, P.O. Box 4192 
Anaheim, California 92803-4192 
Saino 1983, Shimazu 1983), JETR is designed to 
translate ungrammatical and abbreviated sentences 
using semantic and contextual information. Unlike 
other knowledge-based translation systems 
(Cullingford 1976, Ishizaki 1983, Schank 1982, Yang 
1981), JETR does not view machine translation as a 
paraphrasing problem. JETR attempts to achieve 
semantic, pragmatic, structural and lexical invariance 
which (Carbonell 1981) gives as multiple dimensions 
of quality in the translation process. 
Sends phrases, wood 
d~sses and phrase roles \[Analyzer\[ 
(PDA) ,~ 
Sends object frames 
Sends object framms 
and action frames 
Sends modified expectations, 
modified object types and 
filled frames 
Generator 
I 
Resolves Sends 
an~hofic object 
references frames 
I Context Analyzer I 
Figure 1. JETR Components 
JETR is comprised of three interleaved components: 
the particle-driven analyzer, the generator, and the 
context analyzer as shown in Figure 1. The three 
components interact with one another to preserve 
information contained in grammatical as well as 
ungrammatical texts. The overview of each component 
is presented below. This paper focuses on the particle- 
driven analyzer. 
CIIARACTERISTICS OF TilE JAPANESE LANGUAGE 
The difficulty of translation depends on the 
similarity between the languages involved. Japanese 
and English are vastly different languages. Translation 
from Japanese to English involves restructuring of 
sentences, disambiguation of words, and additions and 
25 
deletions of certain lexical items. The following 
characteristics of the Japanese language have 
influenced the design of the JETR system: 
1. Japanese is a left-branching, post- 
positional, subject-object-verb language. 
2. Particles and not word order are important 
in determining the roles of the noun 
phrases in a Japanese sentence. 
. Information is usually more explicitly 
stated in English than in Japanese. There 
are no articles (i.e. "a", "an", and "the"). 
There are no singular and plural forms of 
nouns. Grammatical sentences can have 
their subjects and objects missing (i.e. 
ellipses). 
PDA: PARTICLE-DRIVEN ANALYZER 
Observe the following sentences: 
Verb-deletion: 
Neji (screw) o (object marker) migi (right) e 
(direction marker) 3 kurikku (clicks). 
Particle-deletion: 
Shin (salt) keiniku (chicken) ni (destination 
marker) furu (sprinkle). 
The first sentence lacks the main verb, while the 
second sentence lacks the particle after the noun 
"shin." The role of "shin" must be determined 
without relying on the particle and the word order. 
In addition to the problems of unknown words and 
unclear or ambiguous interpretation, missing particles 
and verbs are often found in recipes, instruction 
booklets and other informal texts posing special 
problems for machine translation systems. The 
Particle-Driven Analyzer (PDA) is a robust 
intrasentence analyzer designed to handle 
ungrammatical sentences in an elegant and efficient 
manner. 
While analyzers of the English language rely 
heavily on verb-oriented processing, the existence of 
particles in the Japanese language and the subject- 
object-verb word order have led to the PDA's reliance 
on forward expectations from words other than verbs. 
The PDA is unique in that it does not rely on the 
presence of particles and verbs in the source text. To 
take care of missing particles and verbs, not only 
verbs but all nouns and adverbs are made to point to 
action frames which are structures used to describe 
actions. For both grammatical and ungrammatical 
sentences, the PDA continuously combines and refines 
forward expectations from various phrases to determ/ne 
their roles and to predict actions. These expectations 
are semantic in nature and disregard the word order of 
the sentence. Each expectation is an action-role pair of 
the form (<action> <role>). Actions are names of 
action frames while roles correspond to the slot names 
of action frames. Since the main verb is almost always 
found at the end of the sentence, combined forward 
expectations are strong enough to point to the roles of 
the nouns and the meaning of the verb. For example, 
consider "neji (screw) migi (right) • 3 kurikku 
(clicks)." By the time, "3 clicks" is read, there are 
strong expectations for the act of turning, and the 
screw expects to be the object of the act. 
Input: <muM> o ~ ~ <verb> 
(al ~e~) J2 
(a3 ~$Una~) 
(a4 des~na~on) 
(al oqect) 
(al iN;~ument) 
(a3 destination) 4 
Intersection: 
(a2 oqe~ (~ dasdna~on) (a2 desdnaton) 
Figure 2. Expectation Refinement in the PDA 
Figure 2 describes the forward expectation- 
refinement process. In order to keep the expectation 
list to a manageable size, only ten of the most likely 
roles and actions are attached to each word. 
Input:. 
Expectations: 
<noun1> m 
Intersection: 
(at ~ .(al ~j~ a2 a3 
(~e~ \[ (~o.) 4.. nounl ~e~t'~ 
(a4 deshion). / 
9ene~ole tier 
(at oqd (~) , 
,mp~ 
Figure 3. Expectation Mismatch in the PDA 
The PDA is similar to IPP (Lebowitz 1983) in that 
words other than verbs are made to point to structures 
which describe actions. However, unlike IPP, a generic 
role-filling process will be invoked only if an 
26 
unexpected verb is encountered or the forward 
expectations do not match. Figure 3 shows such a 
case. The verb will not invoke any role-filling or 
role-determining process ff the semantic expectations 
from the other phrases match the verb. Therefore, the 
PDA discourages inefficient verb-initiated backward 
searches for role-fillers even when particles are 
missing. 
Unlike LUTE (Shimazu 1983), the PDA's generic role- 
filling process does not rely on the presence of 
particles. To each slot of each action frame, acceptable 
filler types are attached. When particles are missing, 
the role-filling rule matches the object types of role 
fillers against the information attached to action 
frames. The object types in each domain are organized 
in a hierarchy, and frame slots are allowed to point to 
any level in the hierarchy. 
Verbs with multiple meanings are disambiguated by 
starting out with a set of action frames (e.g. a2 and a3) 
and discarding a frame if a given phrase cannot fill any 
slot of the frame. 
The PDA's processes can be summarized as follows: 
1. Grab a phrase bottom-up using syntactic 
and semantic word classes. Build an object 
frame if applicable. 
2. Recall all expectations (action-role pairs) 
attached to the phrase. 
3. 
4. 
If a particle follows, use the particle to 
refine the expectations attached to the 
phrase. 
Take the intersection of the old and new 
expectations. 
5. If the intersection is empty, set a flag. 
6. 
7. 
If this is a verb phrase and the flag is up, 
invoke the generic role-filling process. 
Else if this is the end of a simple 
sentence, build an action frame using 
forward expectations. 
8. Otherwise go back to Step 1. 
To achieve extensibility and flexibility, ideas such as 
the detachment of control structure from the word 
level, and the combination of top-down and bottom-up 
processing have been incorporated. 
SIMULTANEOUS GENERATOR 
Certain syntactic features of the source text can 
serve as functionally relevant features of the situation 
being described in the source text. Preservation of 
these features often helps the meaning and the nuance 
to be reproduced. However, knowledge-based systems 
discard the syntax of the original text. In other words, 
the information about the syntactic style of the source 
text, such as the phrase order and the syntactic classes 
of the original words, is not found in the internal 
representation. Furthermore, inferred role fillers, causal 
connections, and events are generated disregarding the 
brevity of the original text. For example, the 
generator built by the Electrotechnical Laboratory of 
Japan (Ishizaki 1983), which produces Japanese texts 
from the conceptual representation based on MOPs 
(Schank 1982), generates a pronoun whenever the 
same noun is seen the second time. Disregarding the 
original sentence order, the system determines the 
order using causal chains. Moreover, the subject and 
object are often omitted from the target sentence to 
prevent wordiness. 
Unlike other knowledge-based systems, JETR can 
preserve the syntax of the original text, and it does so 
without building the source-language tree. The 
generation algorithm is based on the observation that 
human translators do not have to wait until the end of 
the sentence to start translating the sentence. A human 
translator can start translating phrases as he receives 
them one at a time and can apply partial syntax- 
transfer rules as soon as he notices a phrase sequence 
which is ungrammatical in the target language. 
Verb Deletion: 
Shio o Ilikiniku hi. 
Mizu wa nabe hi. 
SaJt on ground meat. 
As for the water, in a poL 
Par~cle Deletion: 
Hikiniku, shio o furu. ~ Ground meat, sprinkle sail 
Word Order Preservation: 
o-kina fukai nabe ~ big deep pot 
fukai o-kina nabe ~ deep big pot 
Le~cal ~nveriance: 
200 g no hikiniku o 
itameru. Kosho- o 
hikiniku ni futte 
susumeru. 
Stir-fry 200g of ground 
meat. Sprinkle pepper on 
the ground meat;, serve. 
2009 no hikiniku o 
itameru. Kosho- o 
sore ni futte susumeru. 
Stir-fry 200g of ground 
meat. Sprinkle pepper 
on it; serve. 
Figure 4. Style Preservation In the Generator 
The generator does not go through the complete 
semantic representation of each sentence built by the 
other components of the system. As soon as a phrase 
is processed by the PDA, the generator receives the 
phrase along with its semantic role and starts 
generating the phrase if it is unambiguous. Thus the 
generator can easily distinguish between inferred 
information and information explicitly present in the 
27 
source text. The generator and not the PDA calls the 
context analyzer to obtain missing information that 
are needed to translate grammatical Japanese sentences 
into grammatical English sentences. No other inferred 
information is generated. A preposition is not 
generated for a phrase which is lacking a particle, and 
an inferred verb is not generated for a verb-less 
sentence. Because the generator has access to the 
actual words in the source phrase, it is able to 
reproduce frequent occurrences of particular lexical 
items. And the original word order is preserved as 
much as possible. Therefore, the generator is able to 
preserve idiolects, emphases, lengths, ellipses, syntax 
errors and ambiguities due to missing information. 
Examples of target sentences for special cases are 
shown in Figure 4. 
To achieve structural invariance, phrases are output 
as soon as possible without violating the English 
phrase order. In other words, the generator pretends 
that incoming phrases are English phrases, and 
whenever an ungrammatical phrase sequence is 
detected, the new phrase is saved in one of three 
queues: SAVED-PREPOSITIONAL, SAVED-REFINER, 
and SAVED-OBJECT, As long as no violation of the 
English phrase order is detected or expected, the 
phrases are generated immediately. Therefore, no 
source-language tree needs to be constructed, and no 
structural information needs to be stored in the 
semantic representation of the complete sentence. 
To prevent awkwardness, a small knowledge base 
which relates source language idioms to those of the 
target language is being used by JETR; however, one 
problem with the generator is that it concentrates too 
much on information preservation, and the target 
sentences are awkward at times. Currently, the system 
cannot decide when to sacrifice information 
preservation. Future research should examine the 
ability of human transla~rs to determine the important 
aspects of the source text. 
INSTRA: Tile CONTEXT ANALYZER 
The context analyzer component of JETR is called 
INSTRA (INSTRuction Analyzer). The goal of INSTRA 
is to aid the other components in the following ways: 
I. Keep track of the changes in object types 
and forward expectations as objects are 
modified by various modifiers and actions. 
. Resolve pronoun references so that correct 
English pronouns can be generated and 
expectations and object types can be 
associated with pronouns. 
. Resolve object references so that correct 
expectations and object types can be 
associated with objects and consequently 
the article and the number of each noun 
can be determined. 
4. Choose among the multiple interpretations 
of a sentence produced by the PDA. 
. Fill ellipses when necessary so that well- 
formed English sentences can be 
generated. 
In knowledge-based systems, the context analyzer is 
designed with the goal of natural-language 
understanding in mind; therefore, object and pronoun 
references are resolved, and ellipses are filled as a by 
product of understanding the input text. However, 
some human translators claim that they do not always 
understand the texts they translate (Slocum 1985). 
Moreover, knowledge-based translation systems are 
less practical than systems based on direct and transfer 
methods. Wilks (1973) states that "...it may be 
possible to establish a level of understanding 
somewhat short of that required for question-answering 
and other intelligent behaviors." Although 
identifying the level of understanding required in 
general by a machine translation system is difficult, 
the. level clearly depends on the languages, the text 
type and the tasks involved in translation. INSTRA 
was designed with the goal of identifying the level of 
understanding required in translating instruction 
booklets from Japanese to English. 
A unique characteristic of instruction booklets is 
that every action produces a clearly defined resulting 
state which is a transformed object or a collection of 
transformed objects that arc likely to be referenced by 
later actions. For example, when salt is dissolved into 
water, the salty water is the result. When a screw is 
turned, the screw is the result. When an object is 
placed into liquid, the object, the liquid, the container 
that contains the liquid, and everthing else in the 
container are the results. INSTRA keeps a chain of the 
resulting states of the actions. INSTRA's five tasks all 
deal with searches or modifications of the results in 
the chain. 
- bgreoients - 
OBJ RICEV~IT 3 CUPS~ALIAS INGO 
OBJ WING~DJ CHICKEI~MT 100 TO 120 GRAMS~LIAS ING1 
OBJ EGGV~MT 4~,LIAS ING2 
OBJ BAMBOO:SHOOT~DJ BOILEDV~.MT 40 GRAMSU~IAS ING3 
OBJ ONIONV~.DJ SMALL~AMT I~LIAS ING4 
OBJ SHIITAKE:MUSHROOMV~DJ FRESH~AMT 2~ALIAS INGS 
OEJ LAVERV~MT AN APPROPRIATE AMOUNT~,LIAS ING6 
OBJ MITSUBA'tAM'T A SMALL AMOUntS ING7 
- the rk:e is bo\]h~:l - 
STEP10BJ RICE~,LIAS INGOV~T I~EFPLURAL T 
- the chicken, onion, bamboo shoots, mushrooms and mitsuba ate cut. 
STEP20BJ CHICKEN'tALIAS INGI~RT '1~REF PLURAL T 
STEP20BJ ONION~IAS ING4~ART T 
STEP20BJ BAMBOO:SHOOT ~ALIAS ING3IART T~REFPLURAL T 
STEP2 08J SHIITAKE:MUSHROOM~ FRESHV~LIAS ING5~RT 
REFPLURAL T 
STEP20BJ MITSUBAV~J.IAS INGT~ART T 
Figure S. Chain or State= Used by INSTRA 
28 
To keep track of the state of each object, the object 
type and expectations of the object are changed 
whenever certain modifiers are found. Similarly, at the 
end of each sentence, 1) the object frames representing 
the result objects are extracted from the frame, 2) each 
result object is given a unique name, and 3) the type 
and expectations are changed if necessary and are 
attached to the unique name. To identify the result of 
each action, information about what results from the 
action is attached to each frame. The result objects are 
added to the end of the chain which may already 
contain the ingredients or object components. An 
example of a chain of the resulting states is shown in 
Figure 5. 
In instructions, a pronoun always refers to the result 
of the previous action. Therefore, for each pronoun 
reference, the unique name of the object at the end of 
the chain is returned along with the information about 
the number (plural or singular) of the object. 
For an object reference, INSTRA receives an object 
frame, the chain is searched backwards for a match, and 
its unique name and information about its number are 
returned. INSTRA uses a set of rules that takes into 
account the characteristics of modifiers in instructions 
to determine whether two objects match. Object 
reference is important also in disambiguating item 
parts. When JETR encounters an item part that needs 
to be disambiguated, it goes through the chain of 
results to find the item which has the part and retrieves 
an appropriate translation equivalent. The system uses 
additional specialized rules for step number references 
and divided objects. 
Ellipses are filled by searching through the chain 
backwards for objects whose types are accepted by the 
corresponding frame slots. To preserve semantic, 
pragmatic and structural information, ellipses are filled 
only when 1) missing information is needed to 
generate grammatical target sentences, 2) INSTRA must 
choose among the multiple interpretations of a 
sentence produced by the PDA, or 3) the result of an 
action is needed. 
The domain-specific knowledge is stated solely in 
terms of action frames and object types. INSTRA 
accomplishes the five tasks I) without pre-editing and 
post-editing, 2) without relying on the user except in 
special cases involving unknown words, and 3) 
without fully understanding the text. INSTRA assumes 
that the user is monolingual. Because the method 
refrains from using inferences in unnecessary cases, 
the semantic and pragmatic information contained in 
the source text can be preserved. 
CONCLUSIONS 
This paper has presented a robust expectation-based 
approach to machine translation which does not view 
machine translation as a testhod for AI. The paper has 
shown the need to consider problems unique to 
machine translation such as preservation of syntacite 
and semantic information contained in grammatical as 
well as ungrammatical sentences. 
The integration of the forward expectation- 
refinement process, the interleaved generation 
technique and the state-change-based processing has 
led to the construction of an extensible, flexible and 
efficient system. Although JETR is designed to 
translate instruction booklets, the general algorithm 
used by the analyzer and the generator are applicable 
to other kinds of text. JETR is written in UCI LISP on 
a DEC system 20/20. The control structure consists of 
roughly 5500 lines of code. On the average it takes 
only 1 CPU second to process a simple sentence. 
JETR has successfully translated published recipes 
taken from (Ishikawa 1975, Murakami 1978) and an 
instruction booklet accompanying the Hybrid-H239 
watch (Hybrid) in addition to hundreds of test texts. 
Currently the dictionary and the knowledge base are 
being extended to translate more texts. 
Sample translations produced by JETR are found in 
the appendix at the end of the paper. 
REFERENCES 
AAT. 1984. Fujitsu has 2-way Translation System. 
AAT Report 66. Advanced American 
Technology, Los Angeles, California. 
CarboneU, J. G.; Cullingford, R. E. and Gershman, A. 
G. 1981. Steps Toward Knowledge-Based 
Machine Translation. IEEE Transaction on 
Pattern Analysis and Machine Intelligence 
PAMI, 3(4). 
Cullingford, R. E. 1976. The Application of Script- 
Based Knowledge in an Integrated Story 
Understanding System. Proceedings of 
COLING-1976. 
Granger, R.; Meyers, A.; Yoshii, R. and Taylor, G. 
1983. An Extensible Natural Language 
Understanding System. Proceedings of the 
Artificial Intelligence Conference, Oakland 
University, Rochester, Michigan. 
Hybrid. Hybrid--cal. H239 Watch Instruction Booklet. 
Seiko, Tokyo, Japan. 
Ibuki, J; et. al. 1983. Japanese-to-English Title 
Translation System, TITRAN - Its Outline and 
the Handling of Special Expressions in Titles. 
Journal of Information Processing, 6(4): 231- 
238. 
Ishikawa, K. 1975. Wakamuki Hyoban Okazu 100 Sen. 
Shufu no Tomo, Tokyo, Japan. 
Ishizakl, S. 1983. Generation of Japanese Sentences 
from Conceptual Representation. Proceedings 
of IJCAI-1983. 
Lebowitz, M. 1983. Memory-Based Parsing. Artificial 
Intelligence, 21: 363-404. 
Murakami, A. 1978. Futari no Ryori to Kondate. 
Shufu no Tomo, Tokyo, Japan. 
Nitta, H. 1982. A Heuristic Approach to English-into- 
Japanese Machine Translation. Proceedings of 
COLING-1982. 
29 
Saino, T. 1983. Jitsuyoka • Ririku Suru Shizengengo 
Shori-Gijutsu. Nikkei Computer, 39: 55-75. 
Schank, R. C. and Lytinen, S. 1982. Representation 
and Translation. Research Report 234. Yale 
University, New Haven, Connecticut. 
Shimazu, A; Naito, A. and Nomura, H. 1983. Japanese 
Language Semantic Analyzer Based on an 
Extended Case Frame Model. Proceedings of 
IJCAI-1983. 
Slocum, J. 1985. A Survey of Machine Translation: Its 
History, Current Status and Future Prospects. 
Computational Linguistics, 11(1): 1-17. 
Wilks, Y. 1973. An Artificial Intelligence Approach to 
Machine Translation. In: Schank, R. C. and 
Colby, K., Eds., Computer Models of Thought 
and Language. W. H. Freeman, San Francisco, 
California: 114-151. 
Yang, C. J. 1981. High Level Memory Structures and 
Text Coherence in Translation. Proceedings of 
LICAI-1981. 
Yoshii, R. 1986. JETR: A Robust Machine Translation 
System. Doctoral dissertation, University of 
California, Irvine, California. 
APPENDIX - EXAMPLES 
NOTE: Comments are surrounded by angle brackets. 
EXAMPLE 1 
SOURCE TEXT: (Hybrid) 
Anarogu bu no jikoku:awase. 
60 pun shu-sei. 
Ryu-zu o hikidashite migi • subayaku 2 kurikku 
mawasu to cho-shin ga 1 kaiten shire 60 pun susumu. 
Mata gyaku hi, hidari e subayaku 2 kurikku mawasu to 
cho-shin ga I kaiten shim 60 pun modoru. Ryu-zu o I 
kurikku mawasu tabigoto ni pitt to iu kakuninon ga 
dcru. 
TARGET TEXT: 
The time setting of the analogue part. 
The 60 minute adjustment 
Pull out the crown; when you quickly turn it clockwise 
2 clicks, the minute hand turns one cycle and advances 
60 minutes. Also conversely, when you quickly turn it 
counterclockwise 2 clicks, the minute hand turns one 
cycle and goes back 60 minutes. Everytime you turn 
the crown I click, the confirmation alarm "peep" goes 
off. 
EXAMPLE 2 
SOURCE TEXT: (Murakami 1978) 
Tori no karaage. 
4 ninmac. 
<<ingredients need not be separated by punctuation>> 
honetsuki butsugiri no keiniku 500 guramu 
jagaimo 2 ko 
kyabetsu 2 mai 
tamanegi 1/2 ko 
remon 1/2 ko 
paseri. 
(I). 
Keiniku ni sho-yu o-saji 2 o karamete 1 jikan oku. 
(2). 
Jagaimo wa yatsuwari ni shire kara kawa o muki mizu 
ni I0 pun hodo sarasu. <<wa is an ambiguous 
particle>> 
(3). 
Tamanegi wa usugiri ni shire mizu ni sarashi kyabetsu 
wa katai tokoro o sogitotte hate ni 3 to-bun shite kara 
hosoku kizami mizu ni sarasu. 
(4). 
Chu-ka:nabe ni abura o 6 bunme hodo here chu-bi ni 
kakeru. 
(5). 
Betsu nabe ni yu o wakashi jagaimo no rnizuko o kittc 
2 fun hodo yude zaru ni agete mizuke o kiru. 
(6). 
(1) no keiniku no shirnke o kitte komugiko o usuku 
mabusu. (7). 
Jagaimo ga atsui uchini ko-on no abura ni ire ukiagatte 
kita ra chu-bi ni shi ~tsuneiro ni irozuitc kita ra 
tsuyobi ni shite kararito sasete ageami do 
tebayaku sukuiage agcdai ni totte abura o kiru. 
(8). 
Keiniku o abura ni ire ukiagatte kita ra yowame no 
chu-bi ni shite 2 fun hodo kakem naka made hi o to- 
shi tsuyobi ni shim kitsuneiro ni agcru. <<hi o to-shi 
is idiomatic>> 
(9). 
(3) no tamanegi, kyabetsu no mizuke o kiru. Kyabetsu 
o utsuwa ni shiite keiniku o mori jagaimo to tamanegi 
o soe lemon to paseri o ashirau. 
TARGET TEXT: 
Fried chicken. 
4 servings. 
500 grams of chopped chicken 
2 potatoes 
2 leaves of cabbage 
1/2 onion 
I/2 lemon 
parsely 
(1). 
All over the chicken place 2 tablespoons of soy sauce; 
let alone 1 hour. 
30 
(2). 
As for the potatoes, after you cut them into eight 
pieces, remove the skin; place about 10 minutes in 
water. 
(3). 
As for the onion, cut into thin slices; place in water. 
As for the cabbage, remove the hard part; after you cut 
them vertically into 3 equal pieces, cut into fine 
pieces; place in water. 
(4). 
In a wok, place oil about 6110 full; put over medium 
heat. 
(5). 
In a different pot, boil hot water; remove the moisture 
of the potatoes; boil about 2 minutes; remove to a 
bamboo basket; remove the moisture. 
(6). 
Remove the moisture of the chicken of (1); sprinkle 
flour lightly. 
(7). 
While the potatoes are hot, place in the hot oil; when 
they float up, switch to medium heat; when they turn 
golden brown, switch to strong heat; make them 
crispy; with a lifter drainer, scoop up quickly; remove 
to a basket; remove the oil. 
(s). 
Place the chicken in the oil; when they float up, 
switch to low medium heat; put over the heat about 2 
minutes; completely let the heat work through; switch 
to strong heat; fry golden brown. 
(9). 
Remove the moisture of the onion of (3) and the 
cabbage of (3); spread the cabbage on a dish; serve the 
chicken; add the potatoes and the onion; add the lemon 
and the parsely to garnish the dish. 
31 
