Word Expert Translation from German into Chinese in - Knowledge ...

Word Expert Translation from German into Chinese
in the Slow Intelligence Framework
Abstract—This paper presents a novel approach to translating
German sentences into Chinese using word expert translators,
thereby extending the application area of the slow intelligence
architecture. The word expert perspective to natural language
understanding is reviewed. The motivation to word expert
translation is presented – It is shown in detail that the Chinese
language depends crucially on the topic-comment relation and is
more suitable to be understood from the word expert perspective.
Five principles for communication among Chinese word experts
are proposed. Main activities of word expert translators consist
of enumerating possible Chinese lexemes of a German lexeme,
determining linear ordering among Chinese lexemes within one
word expert translator, determining topic-comment relations
among word experts, constructing nested topic-comment relations
among word expert translators, choosing the possible Chinese
lexemes. All these fit well within the slow intelligence framework.
I. INTRODUCTION
Machine translation (MT) is to transform texts from one
natural language into another by computers. The worldwide
market for machine translation is large and steadily increasing
with a growth rate of around 20% per year, in part because
the manual translation by human translators is expensive and
slow. Main technologies for MT are the interlingua method, the
statistical method, and the rule-based approach. The rule-based
method assumes that languages can be governed by rules.
However, every rule has exceptions; and the mapping between
rules of different languages can be complicated, sometimes
even impossible. The statistical method [1] focuses on the
probability of translating a sentence in the source language
into a sentence in the target language, therefore, it requires a
highly qualified and extensive sample translation corpus. Even
if such sample corpus is available, what the statistical method
guarantees is only a probability. The interlingua approach
ideally assumes a common semantic representation for all
natural languages. Translation is a process of transforming
text in the source language into the interlingua, and a process
of transforming the interlingua into the target language. The
difficulty is that such interlingua is hard to develop, at least at
the time when the ALPAC report was made. Recent research
on the semantic representation of natural language has made
fruitful progress. It is time now to reconsider MT with the
interlingua approach, e.g. [8]. The present paper adopts the
MultiNet representation [5] as the interlingua for the semantic
representation of natural languages, and applies the word
expert perspective for machine translation.
Tiansi Dong and Ingo Glöckner
Department of Mathematics and Computer Science
University of Hagen
Email: {tiansi.dong|ingo.gloeckner}@fernuni-hagen.de
The rest of the paper is structured as follows: Section
2 reviews the word expert perspective, and the successful
application of the WOCADI parser developed by [6]; Section 3
presents the topic-comment structure of the Chinese language,
and motivates the method of word expert translator; Section
4 presents the main activities of word expert translators
and shows how these activities are carried out in the slow
intelligence framework.
II. WORD EXPERT PERSPECTIVE
A. The Perspective
The traditional perspective views words as passive data
(with knowledge of part of speech and meaning), and languages
as an infinite set of sequences of words (satisfying
grammatical rules). The word expert perspective pioneered by
Rieger [9] views individual words as active procedures – Each
word of language is seen as an active lexical agent called a
word expert, which participates in the overall control of the
parsing process by its internal actions and its interactions with
other such agents [10, p.1]. The word expert view advocates
the integrated syntax-semantics coupling approach to language
understanding. The traditional syntax is viewed as an artifact
describing patterns of lexical interactions, and cannot be used
to model comprehension due to the rich semantic particularities
of lexemes [10, p.3]. The word expert perspective
views text understanding as a process of interactions among
word exports that results in a disambiguation. The work of
parsing is designed as the decision-making process of each
word expert to choose one suitable interpretation for the word
that it represents. Comprehension is therefore simulated as
an activity of looking for the best possible fit among word
experts. This view not only differs from the traditional rulebased
approach (in which words are passive), but also from
the statistical-based approach (in which best fit is guessed by
looking back into the past existing sample corpus).
B. Word Expert Parser for English
Following Wilks’ parsing system [13], Small [10] developed
one of the most influential word expert parsers for English. His
theoretical position is that words have no meaning per se, but
rather that fragments of lexical items mean something through
their interrelationships [11, pp.70]. That is, each lexical item
is viewed as having certain interactions with its neighborhood
items, and produces meanings.

C. Word Class Expert Parser for German
Based on the ideas of Word Class Functions [7], Helbig
and Hartrumpf [6] developed the first semantically oriented
word class expert parser for the German language – WOCADI.
In contrast to Small’s distributed-interaction approach, WCFA
describes the grammatical functions of whole classes of words
[6, pp.313]. The parser transforms German sentences into
the MultiNet formalism [5]. It has been tested with all of
the texts in the German Wikipedia. The MultiNet formalism
and the WOCADI parser have been successfully applied
in the LogAnswer question answering (QA) system which
scored second among non-English QA systems in the CLEF
competition [4].
D. Word Experts for Translation
As pointed out by Small, the word expert perspective
suggests a new way to look at translation [10, p.15]. The
generation step, in particular, requires word experts to arrange
themselves into a meaningful sequence by communicating
with each other. 1 For example, let the three word experts
represent drive, Joanie and car. The drive expert would send
out the message: in front of me there shall be someone, behind
me there shall be a vehicle, who fits? The Joanie expert would
reply: I can stay in front of you; the car expert would reply:
I can stay behind you. A sequence of Joanie drives car is
therefore formed.
Translation from the word expert perspective consists of
two processes: one process is word expert parsing from the
source language into a meaning representation, and the second
process is word expert generation from the meaning representation
into the target language. We focus on the translation
from German into Chinese. The first process is carried out
by the WOCADI parser. The second process transforms the
MultiNet semantics representation [5] into Chinese.
III. THE CHINESE LANGUAGE AND CHINESE WORD
EXPERTS
The grammar for Chinese is totally different from the
grammars of German or English: Most parts of speech can
serve as both the subject and the predicate in a Chinese
sentence. Therefore, an attempt to map grammar rules between
German and Chinese only makes things complicated. We will
show that the word expert perspective is a very suitable way
to explain the Chinese language.
A. Subject and Predicate as Topic and Comment
Chao [3] studied the Chinese language and concluded that
the relation between the subject and the predicate in a Chinese
sentence is a topic-comment relation. This relation holds in all
Chinese dialects [12], e.g., Mandarin (used in Peking area),
Wu (used around Shanghai area), Cantonese (used in and
around Canton area), and WenYan (used in ancient China).
For example, in ����(��/John, ��/dead), ��/John
is the topic, ��/dead is the comment, which presents a
1 For simplicity, we adopt Small’s individual word experts view here though
an abstraction of word class experts would again make sense.
comment on ��, which means he is dead. In�����
�(��/John, ��/dead, ��/father), ��/John is the topic,
����(��/dead, ��/father) is the comment, which
presents a comment on ��, which means his father is dead.
In ���(�/water,��/boil), �/water is the topic, ��/boil
comments on the water; in ����(�/book, �/read, �
�/finish), �(book) is the topic, ���(�/read, ��/finish)
is a comment – (I) have finished reading the book.
The topic-comment relation introduces a question-answer
relation between the subject and the predicate [3, p.81].
Imagine a man returns home after work, and asks his wife,
��? (where (is the) rice?) his wife answers, ����(all
eaten). The man introduces a topic �/rice in a question; his
wife comments on the topic by answering all eaten.
B. Full Chinese Sentences
A full Chinese sentence has a topic and a comment.
1) nominal expressions as comments: In ����(he is
an American), �/he is a pronoun, ���/American is a
noun. There is no link verb �/is between �/he and �
��/American. The Chinese Word Expert � (shortened as
‘CWE�’) asks its surrounding experts, for example, who can
be my property? The CWE��� answers: I can be your
property of nationality. If we view Chinese words as such
active agents, instead of passive data as in the traditional rulebased
grammar, the link verb �/be is not necessary. In ���
���(��/inside of the room, ��/many, ��/mosquito),
the nominal expression ���� is the predicate. The CWE�
� asks: what is inside of the room? The CWE����
answers: many mosquitoes. The meaning of the sentence is
there are many mosquitoes inside of the room.
2) active verbs as comments: In �������(this
matter has long been published), ���(this matter) is the
topic, ��/publish is a verb in the active form. In Chinese the
passive form ���(be published) may not be used to mark
the passive action – a construction taken for granted in English
or German. However, from the word expert perspective, Chinese
has more pragmatic efficiency in expressing meanings:
the CWE�� asks: what can be published? the CWE���
answers: this matter. So, in Chinese, the passive form is indeed
not necessary. In ���,��(wine, (I) do not drink, tobacco,
(I) smoke), �/wine and �/tobacco are subjects, �/drink and
�/smoke are verbs in the active form. The CWE�� asks:
what not to drink? the CWE� answers: wine. The CWE�
asks: what to smoke? the CWE� answers: tobacco.
3) adjective as comments: In � �(�/I, �/poor), the
whole comment is one adjective word �. The meaning of the
sentence is I am poor. The CWE� asks: how about me? The
CWE� answers: poor. In ��(�/dish, �/salty), the whole
comment is one adjective word �. The CWE� asks: who can
serve my property? The CWE� answers: salty. The meaning
of the sentence is the dish is salty.
4) full sentences as comments: In ������(��/this,
�/man, ��/ear, �/soft), the comment is a full sentence �
��. The direct translation is (as for) this man, the ear is soft.
The CWE��� and the CWE�� ask: what is my property?

The CWE� answers to both: soft. As the CWE� is nearer
to the CWE�� than to the CWE���, its answer was first
accepted by the CWE��. As a result a new word expert
CWE��� is formed which means gullible. This new expert
answers the question raised by CWE���. The meaning of
the sentence is this man is gullible. From this example we
propose two principles for communications among Chinese
word experts as follows:
Chinese WE Principle 1: Neighborhood word experts have
the priority in communication.
Chinese WE Principle 2: New word experts may appear
after successful communications and play roles in communication
with other experts.
In � ��� ���(� �/this, �/man, � �/mind, �
�/simple), the comment is a full sentence ����. The
direct translation is this man (is such that his) mind is simple.
The meaning of the sentence is the mind of this man is simple.
With the two principles, the CWE�� first communicates
with the CWE��, and forms a new word expert CWE���
�, which answers the question of the CWE���. The whole
process can be simulated by two question-answer rounds: -
how about the mind? -simple -how about this man? -mind is
simple.
5) verbal expressions as topics: In ��,����(�/go,
�/all right, �/not, �/also), verbal expressions � and ��
are topics. The meaning of the sentence is to go is all right, not
to go is also all right. Primitive verbs as subjects are neither
allowed in English nor in German, but very normal in Chinese,
and can be easily explained from the word expert perspective.
The CWE� asks: shall I perform? The CWE� answers: all
right. The CWE�� asks: can I not perform? The CWE�
answers: all right.
6) Spatial-temporal expressions as topics: In ���(�
�/today,�/cold), the temporal expression ��is the topic.
The meaning of the sentence is today is cold. The CWE��
asks: how is today? The CWE� answers: cold. In ���
��? (��/here,�/is,��/where), the spatial expression �
�is the whole topic. The meaning of the sentence is where
is here? The CWE�� asks: where is here? The CWE��
will communicate with word experts in the next sentence for
an answer.
7) conditional expressions as topics: In �������
�����(�/he,��/dead,��/if,��/simply,����
�/unthinkable), the topic is the conditional expression ��
���. The meaning of the sentence is the supposition that
he should die is simply unthinkable. The CWE� asks: how
is he? The CWE�� answers: dead. The CWE�� asks:
what will be the result under what condition? The CWE��
� serves the condition, the CWE���� serves the result.
8) prepositional expressions as topics: In � � � � �
��(�/through,��/chairman,��/convene,��/meeting),
the topic is a prepositional expression ���. The meaning of
the sentence is the meeting is convened through the chairman.
The CWE� asks: through what? by whom? The CWE�
� answers: chairman. A new word expert CWE��� is
formed. The CWE�� asks: who convenes what? how to
convene? The CWE��� answers the how question, the
CWE� � answers the who question, and the CWE� �
answers the what question.
9) full sentences as topics and comments: In �����
��(�/he, ��/dead, �/I, �/awfully, ��/feel bad), the
topic is a full sentence ���(he is dead), the comment is
also a full sentence ����(I feel awfully bad). The direct
translation is he is dead, I feel awfully bad. The meaning of
the sentence is that he is dead is something about which I
feel awfully bad. The CWE� asks: how is he? the CWE�
� answers: dead. The CWE��� asks: how? what is the
result? The CWE� asks: how about myself? The CWE�
�� answers: awfully bad. The CWE���� answer the
question: what is the result? If the sentence is ���, �
�(�/he, ��/dead, ��/traffic accident), the CWE�� will
answer the question of the CWE���: how come?
We conclude that structures of full Chinese sentences violate
many important grammar rules of Western languages, and
that understanding full Chinese sentences can be achieved by
communications among Chinese word experts following some
communication principles. The meaning of a full Chinese
sentence can be represented by a dialog process among the
word experts.
C. Minor Sentences
In conversations, it is normal that one speaker introduces
a topic and the other makes a comment. This introduces the
term of minor sentence [3, p.60]. In contrast to full sentence,
a minor sentence does not have both topics and comments.
The above conclusion is supported by the structure of Chinese
minor sentences in that a speaker in a conversation may only
say a few words to answer the questions raised by word experts
of the other speaker.
D. Compound and Complex Sentences
By parallelizing two or more sentences, we can construct
compound Chinese sentences. For example, �����, �
����(you do not know me and I do not know you) is a
compound sentence by parallelizing two ‘A ��� B’ (A
does not know B) sentences. By nesting one full sentence
either in topic or in comment, we can construct complex
sentences. For example, �������(�/I,��/dead,�
�/funeral,��/simple)is a complex sentence, meaning when
I die, the funeral should be simple, constructed by using a full
sentence ���(I die) as the topic. Both of the two structures
can be easily explained in the word expert framework with the
second communication principle (Chinese WE Communication
2) and the third communication principle as follows.
Chinese WE Principle 3: A newly formed word expert has
priority over old ones in communication.
With this principle, although both CWE�� and CWE��
� answer the CWE����’s conditional question, CWE�
�� is newly formed and has the priority.
E. Pivotal Constructions
A Chinese sentence with a pivotal construction has normally
two verbs and a nominal expression. This nominal expression

serves as the object of the first verb and as the subject of the
second verb [3, p.124-125]. For example, in ������
�(��/we, �/order, �/he, �/serve, ��/representative),
there are two verbs �/send and �/serve, a pronoun�/he is
the object of �/send and the subject of �/serve (in Chinese
�/he has the same form as nominal case and as dative case).
This direct translation of the sentence is that ‘we order he
serve as representative’. The meaning is that we delegate
him to be representative. Within the word expert perspective,
we can explain this special construction without introducing
any new terminologies (as done in the rule-based grammar
theories). CWE� asks: whom is sent? CWE� asks: who
serves? CWE� answers to both: �/he.
IV. GERMAN-CHINESE WORD EXPERT TRANSLATORS
Given a sentence in German, the WOCADI parser can be
used to generate a corresponding semantic representation in
the MultiNet formalism. We need to design word expert translators
that communicate with each other, linearize themselves
to form a (nested) topic-comment structure, and transform the
MultiNet representation into Chinese sentences.
We start with a simple example to introduce the main idea.
Suppose the German sentence is Er ist ein Deutscher (He is a
German). The WOCADI parser delivers the MulitNet semantic
representation as illustrated in Figure 1: er.1.1 is the word
sense2 of the lexeme er/he; c275 represents the word expert
for a concrete individual3 subordinate (SUB) to er.1.1;
similarly, c287 represents the word expert whose concept
is subordinate to deutsche.1.1/German. The word expert
c278 has two arguments: one is the topic, c275, pointed by
ARG1; the second is the comment, c287, pointed by ARG2;
the temporal status (TEMP)ofc278 is present (present.0).
The word expert c275 posts a message: if I am the topic,
who can be my comment? The word expert c278 answers:
as far as I know, the word expert c287 is your comment. If
word expert c275 knows that his Chinese lexeme is �/he,
and word expert c287 knows that his Chinese lexeme is �
��/German, they will know that in the linearization of the
Chinese sentence c275 is before c287. The simplest case
is: ����(He German), which is indeed a valid Chinese
sentence with the same meaning as Er ist ein Deutscher. If
word expert c278 knows that his Chinese lexeme is �/is, and
knows that I shall stay between the topic and the comment
in the Chinese sentence, the Chinese sentence will be ��
���(he is German). If c278 knows that ��/now is
the Chinese lexeme meaning present and decides that his
temporal knowledge shall also be encoded in the Chinese
sentence, the Chinese sentence could be �������,
�������, �������, �������. Ifhe
knows that his temporal label shall be in front of him, the
two linearizations will be ������� and �����
��, both are valid Chinese sentences. These word experts
are now also experts for translating. Activities of each word
2 Our lexicon uses a double indexing scheme to distinguish word senses
3 Entities mentioned in the text are represented by constants c1, c2 etc.
Fig. 1. MultiNet representation of the sentence Er ist ein Deutscher (he is
a German)
expert translator comprise: to transform its German lexeme
into possible Chinese lexemes, to determine a linear ordering
relation among Chinese lexemes of a single word expert
translator, to determine whether its Chinese lexemes shall
appear in the Chinese translation, to establish a linear ordering
relation with other word expert translators by communication,
and to choose most appropriate Chinese lexemes.
A. Transforming into Chinese Lexemes
This task is to find Chinese lexemes for a given German
lexeme such that they represent the same concept. This may
not be always feasible. Some German lexemes may not
have a corresponding native Chinese lexeme, e.g. names of
Cheeses, beers, and chocolates – in German, Franziskaner
can refer to a kind of beer, and there is no corresponding
native Chinese lexeme. For those having corresponding native
Chinese lexemes, these may be different in Chinese dialects.
For example, German lexeme wir (we) can be mapped to ��
in Mandarin, �� in Shanghai dialect, �� in Canton dialect.
Chao [3] suggest that a complete lexicon shall be constructed
to make selection applicable in grammar. For the translation
from German into Chinese, we need to embed the Chinese
lexical ontologies system into the German lexical ontologies
system, and mark each Chinese lexeme with its dialect group,
while neglecting fine Chinese ontologies lexemes which do
not have corresponding German lexemes. German lexemes
which do not have native Chinese lexemes will be translated
separately.
B. Lexeme Ordering within a Word Expert Translator
A word expert in the analysis of a German sentence may
have a temporal property representing the tense. In Chinese
tense is achieved by particles: � � � � �, such as �
���� (er hat gegessen/he has eaten). The word expert
essen.1.1/eat has a temporal property past.0, which shall
be translated into the Chinese particle � and �. The corresponding
Chinese lexemes of word expert essen.1.1 are ��
���, whose linear ordering in Chinese sentence is stated
as follows.
Chinese WE Principle 4: Let L be a word expert which
may have � or �as particle. (1) if � is the only particle,
it must occur directly after L; (2) if � is the only particle, it

Fig. 2. MultiNet representation of the sentence Das Kind hat Angst und
fängt an zu weinen (The Child is scared and begins to cry)
shall be after L; (3) if both � and �are particles, � shall be
before �besides obeying rules (1) and (2); (4) if � is used
twice, besides obeying rule (2), they must be separated by
lexemes of another word expert, and one must directly follow
L.
With the above principle, the following orderings are all
understandable translations: ���– (1), ���– (2), ��
�– (2), ����– (1)(2)(3), ����– (1)(2)(3), ���
�– (2)(4).
C. Determining to Appear or Not
Not all the word experts of a German sentence shall
appear in the translated Chinese sentence. For example, the
German sentence Das Kind hat Angst und fängt an zu weinen
(The Child is scared and begins to cry) shall be translated
into Chinese �����(��/Kind/Child, �/Angst/scare,
�/weinen/cry, �/particle), as illustrated in Figure 2. The following
word experts appear in the translated Chinese sentence:
kind.1.1/child (c97), weinen.1.1/cry (c138), angst.1.1/scare
(c101), and haben.1.1/have (c98). The carrier (SCAR) of
haben.1.1/have is kind.1.1/child, which is the actor (AGT)
of both weinen.1.1/cry and anfangen.1.2/begin. The affected
object (AFF) of anfangen.1.2/begin is weinen.1.1/cry, which
is the second argument (ARG2) of anfangen.1.1. 4
Considering the topic-comment relations among the word
experts, we find that the comment of angst.1.1/scare is
weinen.1.1/cry, and that the two word experts anfangen.1.2/begin
and anfangen.1.1/begin simply duplicate this
topic-comment relation. Therefore, they shall not appear in
the translated Chinese sentence – the redundancy is only an
artifact of our deep semantic analysis. A principle is stated as
follows.
Chinese WE Principle 5: A word expert shall not appear
in the translated Chinese sentence, if it duplicates an existing
topic-comment relation.
4 This analysis reflects that if someone starts something (anfangen.1.1), then
this starting actions causes something to start (anfangen.1.2).
D. Ordering of Word Expert Translators
For those word experts that will appear in the translated
Chinese sentence, a nested topic-comment ordering shall
be constructed. As we have semantic representations from
WOCADI and lexemes tagged with semantic roles, topiccomment
relations among word experts are not difficult to
obtain – we only need to examine the description of the lexeme
and the possible MultiNet constructions, to see which elements
can be the comment of which other elements in the meaning
representation. In Figure 2, angst.1.1/scare is the topic for
weinen.1.1/cry, so we have a list (WE-angst.1.1/scare WEweinen.1.1/cry),
kind.1.1/child is the topic for angst.1.1/scare,
so we have (WE-kind.1.1 (WE-angst.1.1 WE-weinen.1.)). By
flattening this nested structure, we have a linear ordering of the
word experts: (WE-kind.1.1 WE-angst.1.1 WE-weinen.1.1).
Chinese sentences can be obtained by replacing each word expert
with their Chinese lexeme(s). For example, �����,
�����, and ������ are all understandable translations,
where � is the particle of �, whose ordering follows
Chinese WE Principle 4.
E. Choosing Chinese Lexemes
A word expert may have more than one corresponding
Chinese lexeme even in one Chinese dialect. Communications
among word experts are required to select the most suitable
one, or to delete incompatible ones. For example, the word
expert ein.1.1/a in the German phrase ein Baum (a tree) can be
mapped to �����������.... By communicating
with the word expert Baum.1.1/tree, word expert ein.1.1/a
knows that it can only be mapped to ��. This requires word
experts of countable objects to have measurement information.
F. A Slow Intelligence (SIS) Workflow
The word expert translation system can be organized in
the slow intelligence architecture [2]. Each word expert that
occurs in the German parse is a unit slow intelligence system.
It enumerates possible Chinese lexemes, determines possible
linear orderings among themselves, and the topic-comment
relation of its arguments. Word experts communicate among
themselves and form a larger slow intelligence system: nested
topic-comment structures are firstly enumerated, then duplicated
topic-comment relation will be removed, incorrect
lexemes will be pruned, ordering of particles will be linearized
with lexemes of other word experts. At last possible Chinese
sentences will be produced.
For the German sentence Ich fällte einen Baum mit einer Axt
(I cut down a tree with an ax), the MultiNet analysis results
in four word experts: WE-ich.1.1/I (abbreviated WE-I in the
following), WE-baum.1.1/tree (WE-tree), WE-axt.1.1/ax (WEax),
and WE-fällen.1.1/cut-down (WE-cut-down). The system
first creates one CWE for each word expert WE. All the
CWEs are unit SIS, searching for possible Chinese lexemes
and enumerating linear orders based on CWE principles. In our
current German-Chinese dictionary CWE-I has one Chinese
lexeme: �; CWE-cut-down has five Chinese lexemes: ��
� � � � � � � � � � and two particles � and �;

Fig. 3. Word expert translation results for the input sentence Ich fällte einen Baum mit einer Axt
CWE-tree has one Chinese lexeme: ���; CWE-ax has
one Chinese lexeme: �. Only CWE-cut-down need to carry
out an enumeration-elimination process to linearize orders of
each Chinese lexeme and particles. The orders among the
five CWEs will be enumerated based on the topic-comment
relations as follows: (1) CWE-I is the actor of CWE-cutdown,
therefore, CWE-I is the topic, CWE-cut-down is the
comment; (2) CWE-tree is the affected object of CWE-cutdown,
therefore, CWE-cut-down is the topic and CWE-tree is
the comment; (3) CWE-ax is analysed as the association of
CWE-tree, whose features delivered from WOCADI parser are
different: CWE-ax is an instrument, while CWE-tree is a noninstrument.
Therefore, CWE-ax is the instrument commented
both on the action and on the actor. This introduces the particle
�/using before the lexeme of CWE-ax: ��(using an ax).
Two nested topic-comment structures are (CWE-I ((CWEcut-down
CWE-tree) CWE-ax) and (CWE-I (CWE-ax (CWEcut-down
CWE-tree)). In the adaptation phase CWEs will
communicate with each other to prune lexemes. �� and �
� of CWE-cut-down are removed, as they incompatible with
the instrument CWE-ax. A demo translation system has been
developed. Its translation results are illustrated in Figure 3.
V. CONCLUSIONS
The work reported in this paper is mainly based on a classic
research work on Chinese grammar [3]. We argue that the
Chinese language structure is more suitable to be understood
from the word expert perspective than from the traditional rulebased
perspective. The machine translation from German into
Chinese is outlined from the word expert perspective. A slow
intelligence workflow for machine translation is illustrated
with an example.
ACKNOWLEDGMENTS
Hermann Helbig introduced us the topic and critically
commented on the draft paper; Peiling Cui criticized the
Chinese translations. Financial support from DFG is gratefully
acknowledged.
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