WO1999008202A2

WO1999008202A2 - Interlingual translation system and method

Info

Publication number: WO1999008202A2
Application number: PCT/GB1998/002389
Authority: WO
Inventors: Stephen Clifford Appleby
Original assignee: British Telecommunications Public Limited Company
Priority date: 1997-08-08
Filing date: 1998-08-07
Publication date: 1999-02-18
Also published as: AU743538B2; CA2297905C; EP1000401A2; AU8737498A; WO1999008202A3; CA2297905A1; GB9716887D0

Abstract

Apparatus for use within a system for translating a document from a source language to at least one target language, comprising means for parsing said document using grammar rules specific to said source language, to derive a source language semantic structure; means for processing said source language semantic structure, to detect the presence therein of semantic elements which are specific to said source language, and to replace said semantic elements with replacement elements which are generic to a plurality of possible said target languages, to generate a generic semantic representation of said document.

Description

TRANSLATION

This invention relates to automatic language translation.

Machine language translators accept input text in a first natural

language (the source language) and generate corresponding output text in a

second natural language (the target language). Such translators may be

classified into two types; those which use a set of translation rules for each

possible pair of source and target languages, and those (relatively rare)

interlingual systems which translate from the source language into a language

independent (interlingual) form, and then from this language independent

form to the target language.

The former system has the disadvantage that as the number of

languages rises, the number of sets of translation rules rises as the square of

the number of languages. The latter approach is difficult to implement, and

can result in unnatural translations, for example with loss of appropriate

emphases.

A prior art document describing an automatic translation system in

which translation into an interlingual form is proposed in J M VAN ZUILEN:

"Het automatisch vertaalsystem DLT" INFORMATIE, vol. 32, no. 2,

February 1990, DEVENTER, NL, pages 183-191, XP000406044. This

document proposes the use of Esperanto, which is a natural language, as the

interlingual form. However, when an interlingual form is ambiguous in

relation to the target language(s), which will be the case when a natural language is used as the interlingual form, the interlingual form itself cannot be

relied upon to provide a complete translation into the target language.

According to one aspect, the present invention provides a machine

translation system utilising the interlingual approach (i.e. generating a

generally language independent intermediate structure) in which modifiers

(e.g. descriptive words or linguistic structures) which are capable, in the

source language, of occupying more than one position are analysed and the

position occupied is recorded. This enables adverbs or adjectives which have

been placed in an unusual position for stress or emphasis to be translated into

correspondingly stressed or emphasised descriptive terms in the target

language.

In another aspect, the present invention provides a machine translation

system for translating between a plurality of languages, in which grammar

rules specific to the source language are applied to generate a semantic

structure corresponding to the input text, and then semantic structures therein

which are not shared by one or more of the target languages are detected and

replaced with more generic structures, to generate an interlingual structure.

This replacement will be referred to later in this document as "abstracting".

This aspect also provides such a translator in which the interlingual structure

is tested for the presence of such generic structures which have specific

versions within the target language not shared by the source or other

languages, and such structures are replaced by the specific structures for the target language, the amended structure thus produced being used to generate

target language text.

In another aspect, a machine translation system provides an

interlingual form which is unambiguous in relation to all of the target

languages the system is able to translate into, in the sense that the interlingual

form corresponds directly, preferably uniquely, to a language-specific

semantic structure in each of the target languages. Where a semantic structure

in the source language text is itself ambiguous in relation to the interlingual

form, a plurality of alternative interlingual structures may be selected between

by interaction with the user in order to provide disambiguation in accordance

with the meaning of the source language structure intended by the user.

In another aspect, the present invention provides a machine translation

system utilising a generally interlingual approach, in which the process of

converting from the source language to the language independent

representation involves a user-interactive disambiguation process which takes

account of the target language(s), to avoid the unnecessary disambiguation of

linguistic elements which are common to the source and target languages.

This can significantly reduce the amount of interaction required by the

user. It may also reduce the complexity of the abstracting process by which

each source language is transformed into the language independent

representation, which would otherwise involve an increasing number of

transformations or rules with the number of target languages; although such rules must be present, only a subset of the rules need be used in any given

translation process.

In yet another aspect, the invention provides a multilingual messaging

system in which a message is transmitted from a first processor to one or more

destination processors via a telecommunications channel, in the form of an

interlingual semantic representation of the message.

Other aspects and preferred embodiments are as described in the

following description and claims.

Embodiments of the invention will now be illustrated, by way of

example only, with reference to the accompanying drawings, in which:

Figure 1 is a block diagram of the language translation apparatus

according to a first embodiment;

Figure 2 is a block diagram showing in greater detail the processes

present in a client terminal forming part of the embodiment of Figure 1 ;

Figure 3 is a block diagram showing in greater detail the processes

present in a server forming part of the embodiment of Figure 1 ;

Figure 4 is a block diagram showing in greater detail the subprocesses

present within a translation process forming part of the embodiment of Figure

3;

Figure 5 is an illustrative diagram showing the formats through which

text passes during the translation process of the embodiment of Figure 1;

Figure 6 is a block diagram showing the databases maintained within

the server of Figure 1 ; Figure 7 is a schematic diagram illustrating the word structure

produced after text pre-processing in the embodiment of Figure 1;

Figure 8 is a diagram illustrating the entity/relationship semantic

structure produced after parsing in the embodiment of Figure 1 ;

Figure 9 is a flow diagram showing schematically the operation of the

server of the embodiment of Figure 1;

Figure 10 is a diagram illustrating a phrase operated upon by the

parser of the server of Figure 1 ;

Figures 1 1a and l ib illustrate two alternative word orders which are

discriminated by the parser of the embodiment of Figure 1 ;

Figure 12 is a block diagram of the language translation apparatus

according to the second embodiment of the invention; and

Figure 13 is a block diagram showing the processes present in server

and destination terminal components of the second embodiment of Figure 12.

First Embodiment

Referring to Figure 1, the present invention may be employed by a

client terminal 100a connected via a telecommunications network 300 such as

the Public Switched Telephone Network (PSTN) to a server computer 200.

The terms "client" and "server" in this embodiment are illustrative but not

limiting to any particular architecture or functionality.

The client terminal comprises a keyboard 102, a VDU 104, a modem

106, and a computer 108 comprising a processor, mass storage such as a hard

disk drive, and working storage, such as RAM. For example, a SUN™ work station or a Pentium™ personal computer may be employed as the client

terminal 100a.

Stored within the client terminal (e.g. on the hard disk drive thereof) is

an operating control program 110 comprising an operating system 1 12 (such

as Windows™), a browser 1 14 (such as Windows Explorer™ Version 3) and

an application designed to operate with the browser 1 14, termed an applet,

116. The function of the operating system is conventional and will not be

described further. The function of the browser 1 14 is to interact, in known

fashion, with hypertext information received from the server 200 via the

PSTN 300 and modem 106. The browser 1 14 thereby downloads the applet

116 at the beginning of the communications session, as part of a hypertext

document from the server 200. The function of the applet 1 16 is to control the

display of received information, and to allow the input of information for

uploading to the server 200 by the user, through the browser 114.

Referring to Figure 3, the server 200 comprises an operating program

210 comprising an operating system 212 such as Unix™, a server program

214 and a translator program 216. The operating system is conventional and

will not be described further. The function of the server program 214 is to

receive requests for hypertext documents from the client terminal 100a and to

supply hypertext documents in reply. Specifically, the server program 214

initially downloads a document containing the applet 116 for the client

terminal 100a. The server program 214 is also arranged to supply data to and receive data from the translator program 216, via, for example, a cgi.bin

mechanism.

The function of the translator program 216 is to receive text from the

client terminal 100a via the telecommunications network 300 and server

program 214; to interact with the user as necessary in order to clarify the text;

and to produce a translation of the text for supply back to the user (in this

embodiment).

Figure 4 shows the component programs of the translator 216. It

comprises a number of sections; one for each language, of which only a first

section 220. relating to a first language (LANG1) and a second section 230

relating to a second language (LANG2), are shown for clarity. Each language

section comprises the following subprograms or modules:

1 ) A text pre-processor (221, 231)

2) A source language parser (222, 232)

3) A source language abstractor (223, 233)

4) A target language de-abstractor (224, 234)

5) A target language generator (225, 235)

6) A target language text post-processor (226, 236)

The functions of each of these components will be discussed in

greater detail below.

Figure 5 illustrates the stages of translation according to this

embodiment. A source language text document (stage A) is received by the

translator from the client terminal 100a.

After operation of the text pre-processor stage (221 ), the result is an

expanded source language text document (stage B). The operation of the pre¬

processor is to replace contracted forms of words (such as "he's" in English,

or "j'ai" in French) with their non-contracted forms.

After operation of the source language parser 222, stage C of Figure 5

is a language-specific semantic structure which represents the input text as an

encoded entity-relationship graph, where the entities are semantic categories

corresponding to the words (in other words, identifying the nouns, verbs and

so on), and the relationships are data relating the entities together (e.g. to

indicate those which are the subjects or objects of others).

After operation of the source language abstractor 223, the result is a

further semantic structure D, similar to the language specific semantic

structure produced at stage C but indicating additionally relationships and data

which substitute the language-specific meanings of some of the structures

represented within the semantic structure C with abstracted structures.

For example, a phrase such as "My name is David" input as source

language text could be represented within a parsed semantic structure by data

indicating ownership of the name by the individual first person, and an

attribute of the name being that it is "David". This is a grammatically correct

expression, from which French or German text could be generated by a

suitable generator such as 235. However, whilst grammatical French or German would be produced,

the meaning would be unclear, since in French the equivalent phrase is "I call

myself ("je m'appelle") and in German the equivalent phrase is "Ich heiβe"

(which is equivalent to "I am called" in English, but for which English lacks a

corresponding verb). Accordingly, the source language abstractor 223

recognises within the parsed semantic structure of the occurrence of structures

which are not directly translatable, such as structures involving personal

names in this example, and replaces those structures with additional data

representing them.

Accordingly, the abstracted semantic structure produced at stage D of

Figure 5 corresponds to a representation of the input text but with the

replacement of specific constructs which are known not to meaningfully

translate into one or more other languages (whether or not those languages are

represented by sections within the translator 216).

The abstracted semantic structure produced at stage D is an

interlingual form which is unambiguous in relation to each of the target

languages which the system is capable of translating into. That is to say that

the interlingual form corresponds uniquely with a language-specific semantic

structure in each of the target languages. As will be explained below, the text

of the source language may itself include ambiguities which are not directly

translatable into the interlingual form. Such ambiguities are dealt with in the

parsing and abstracting process by generating a plurality of alternative

structures in the interlingual form, the selection of the correct alternative structure being made by user interaction. Each of the alternative structures in

the interlingual form may be de-abstracted and regenerated in the source

language to allow the user to understand the alternatives being proposed. The

result, either of the parsing and abstraction process or the parsing and

abstraction process in combination with user input, is a single structure in the

interlingual form which is entirely unambiguous in relation to the target

languages.

The abstracted semantic structure, or a selected one of the abstracted

semantic structures, produced by the abstractor in stage D is then passed to the

de-abstractor 234 of the target language, which comprises a series of rules

which test for the presence of the additional structures inserted by the

language abstractor 223, and translate them into the form used in the target

language. For instance, in the example given above, the abstracted naming

operation would be converted, in French, into "je me appelle" (I call myself).

The result is then, at stage E, a semantic structure equivalent to the language-

specific semantic structure at stage C but in which the semantic substructures

corresponding to phrases or expressions in the input text which would give

rise to translation difficulties have been replaced by appropriate substructures

in the target language. This structure forms the input to the target language

generator 235, which generates a corresponding target language output text

(stage F), and therefore applies the reverse process to the parsers 222, 232.

Finally, the generated output text at stage F is contracted by the text

post-processor 236 which takes the generated text and contracts relevant parts of it. In the above example, "je me appelle David^" would be contracted to "je

πfappelle David". Other minor text processing operations, such as adding

capital letters at appropriate places (for example at the beginning of each

sentence), and providing the correct spacings between words, are also carried

out.

Referring to Figure 6, the server 200 stores data for use by the parser

and abstractor in each language. This data comprises, for each language, a

grammar rules database (227, 237) and an abstraction rules database (228,

238). Also present is a multilingual lexical database 240. The lexical

database 240 stores an entry for each word in any language represented within

the translator program, the entry stating the languages within which that word

exists, and giving, for each of those languages, the text in the language

concerned; the type of lexical element represented by the word (e.g. whether it

is a noun, a verb, a pronoun, an adjective and so on); data on the manner in

which the word is inflected, if at all, in each language, and various other data.

The grammar rules stored within each grammar rules database (227,

237) represent, for the corresponding language, the ways in which words of

that language may combined. Accordingly, in English, one rule will indicate

that a verb such as "to see" requires an object and a subject, and that in the

active form the subject is the active participant or "agent" (the person who

sees) and the object is the passive participant or "patient" (the thing which is

seen). The abstraction rules will be discussed in greater detail below. The operation of this embodiment will now be disclosed in greater

detail with reference to Figures 7-11.

Referring to Figure 9, in a step 402, text is received from the client

terminal 100a. In a step 404, the input text is expanded. As a first step, the

start and end of each possible word in the text is located by detecting spaces

and punctuation, so as to result in a stream of possible words. As a second

step, any contracted words (such as "j'ai" in French") are expanded to replace

them with full words (in that example, "je ai"). At the same time, the text pre¬

processor locates and flags special text items such as proper names, dates,

times, sums of money and so on.

At this stage, there may be several possible expanded strings of words

that could match each contracted string of word. All such possibilities are

retained as alternatives.

Next, each word is looked up in the lexical database 240, and words

which are not recognised but are closely matched to others in the source

language (that is, the language of the input text) are replaced by all those for

which they are a close match, as in the manner of a conventional spell

checker.

If, after spell checking, any words have not been recognised (step 405)

then a query is transmitted back to the user, comprising a text message saying,

for example, "The word (unrecognised word) has not been recognised. Please

check the spelling, and resubmit this word or a synonym" . This query is then

transmitted to the client terminal 100a in step 406. The result of this pre-processing is therefore that the expanded text

(stage B of Figure 5) is no longer necessarily a linear sequence of words but

may, as shown in Figure 7, comprise a network or lattice of words.

Figure 7 indicates such a network in which the second word, originally

B, has been replaced by two possible alternatives (either alternative spellings

or alternative expansions) Bl and B2, and the third word C has been replaced

by three possible alternatives Cl, C2 and C3. There are thus now six possible

routes through the network of words.

The text of each word in the network is now replaced by a reference to

the corresponding entry in the lexical database 240. If a single word (such as

"bank" in English) has two different entries in the lexical database 240

corresponding to different meanings (which would be translated into different

words in a target language), the word is replaced by each possible entry in the

lexical database 240. For convenience, rather than using references to the

entries in the lexical database, the syntactic category information for each

word (i.e. whether it is a noun, verb etc) may be retained within the network,

and a table relating each network position to the corresponding entry in the

lexical database 240 is separately stored for later use.

In looking up each source language word in the lexical database 240,

further ambiguities may be generated.

For example, English contains a generic word for "nut", whereas

French uses several more specific words. The lexical database 240 contains

the generic entry for the entity "nut", with a record of the English word for that entity. No French word exists. For each specific kind of nut, there is

another entry. For example, the entry for "walnut" has both English and

French equivalent specific words. However, since the English generic word

"nut" can also refer to a walnut, another entry exists for the entity "walnut",

giving the English word "nut" as the English translation. The same is true for

each other specific type of nut. As each of these entries only exists because of

the absence of a generic word for "nut" in French (which would be a direct

translation of the English word), each is noted to be relevant when French is a

target language.

On each such occasion where a single word in the source language is

given as the translation of several different lexical entities in the database 240

(corresponding to several different words in one or more of the target

languages), a reference to each of these is included within the processed text

lattice of Figure 7. Thus, in the above example, when the English word "nut"

is encountered, it is replaced by separate nodes for each entry in the lexical

database 240.

The present invention is intended to enable to translation into multiple

different target languages, and it is apparent that the number of ambiguities

that are thus generated could be substantial. Each ambiguity according to the

present invention is resolved either by discounting possible alternatives as

implausible (for example by contextual analysis using a database of

contextual rules) or by a query to the user to ask him which meaning was

intended. For example, imagine that a document including the word "snow" in

English is to be translated into a notional language which has three different

words for snow; a first word meaning hard snow, a second word meaning soft

snow and a third word meaning snow in general. The source language parser

detects the three different entries in the lexical database 240 for the different

meanings for the word snow, each of which refers to the English word snow

as its translation in English. The query generated comprises text such as

"Please indicate which of the following you mean:

soft snow

hard snow

any snow."

This text is passed to the server program 214 which sends it as a

hypertext form, including areas for selection by the user, to the client terminal

100a, at which the browser 114 displays it. The user then selects the desired

meaning, and the form is returned by the browser 114 to the server 200 and

passed to the parser, which reads the intended meaning and includes the

corresponding lexical item in the subsequent processing stages.

On the other hand, if a given user requires a translation only into one

or more target languages which are as unambiguous as the source language,

then this step of ambiguity resolution will have been unnecessarily

burdensome to the user.

For example, if it is desired to translate text including the word "nut"

from English to German, the fact that the text would be ambiguous in an interlingual representation directly translatable into other languages (for

instance into French), by the retention of the word "nut", is no burden to

translation into German, where a generic term for "nut" ("nuβ") also exists,

which is therefore a direct translation of the English word.

Accordingly, in this embodiment the target languages for translation

are specified by input by the user at the client terminal 100a, and sent to the

server 200 at the outset. Thereafter, only those entries in the lexical database

which exist in the source and all actual target languages into which the text

are to be translated are referred to.

Next, the network of nodes (each corresponding, as noted above, to

one of the entries in the lexical database 240 and being represented by the

syntactic category of that entry) is processed by the source language parser

program, which, for each word, applies the rules within the grammar rules

database 227 which are applicable to words of that type.

Thus, for example, referring to Figure 8, suppose that the English text

contained the phrase "the dog saw the cat". The word "the" is the definite

article, and a rule within the grammar rules database 227 indicates that it can

be followed by the noun to which it refers. Thus, the circle DI indicating the

first occurrence of determiner "the" is linked by this rule to the next circle Nl ,

representing the following noun "cat", and the circle D2, representing the

second occurrence of determiner "the" is linked by this rule to the circle N2

for the following word, which is the noun "cat". The rule for the active form of the verb "to see" indicates that the verb

may be preceded by the seeing "agent" entity (in this case "the dog") and

followed by the patient entity (in this case "the cat").

Thus, after parsing, the parsed semantic structure (stage C of Figure 5)

is represented, for each sentence of the input text, by one or more structures

comprising references to entries in the lexical database 240 (the circles in

Figure 8) and pointers linking them together (the lines in Figure 8). In the

PROLOG computer language, the topological structure of Figure 8 may be

represented as

[

A^Λdet(def,s,_,third),A^Λe(dog,[]),P^Λdet(def,s,_,third),P^Λe(cat,[]),

E^Λevent(see,fin,past,[]),E^ΛA^Λr(agent,[]),E^ΛP^Ar(patient,[])

]

In the foregoing, it will be noted that the unifying variables A and P

are the links which unify the first occurrence of "the" with "dog" and the

second occurrence of "the" with "cat". The verb "see" is linked by an agent

relationship and a patient relationship with the terms linked by the

relationship A (i.e. "the dog") and the terms linked by the relationship P (i.e.

"the cat").

The verb is recorded as an event ("event"), and is linked to the lexical

entry in the lexical database 240 for the word "see" and is indicated to be the

finite form ("fin") in the past tense ("past"). The word "the" is recorded as a determiner, being the definite article

("def '), single rather than plural form ("s"), having neutral gender ("_") and

referring to the third person ("third"). The terms for "dog" and "cat" are

indicated to be entities ("e"), and have a reference to the corresponding word

entry in the lexical database 240.

Thus far, other than the target-language dependency, the parser is not

dissimilar to known, technically and commercially available products.

Further information on suitable chart-parsing techniques which may be used

will be found in James Allen, "Natural Language Understanding", 2nd

Edition, Benjamin Cummings Publications Inc, 1995.

Two respects in which the operation of the parser differs from

conventional parsers will now be described with reference to Figures 10 and

1 1.

Figure 10 illustrates the structure of a phrase such as "the big red bus"

in English, consisting of a determiner (DI), followed by two adjectives (Al,

A2), followed by a noun (Nl) to which the adjectives refer (i.e. which they

modify). During subsequent generation in the target language, it would be

possible to produce parsed structures corresponding to all of the "the bus",

"the big bus", "the red bus", "the big red bus" and "the red big bus".

Only the last two of these would ultimately be accepted as

possibilities, since the others would leave redundant adjectives unaccounted

for. However, a chart-parser-type generator would generate structures for

each of the five possibilities which would, if embedded in a lengthy sentence, lead to a loss of processing speed whilst each possibility was evaluated, even

though ultimately only the last two would lead to generated text.

Furthermore, one of the two possibilities loses something of the meaning of

the original text, since in English the first adjective modifies those which

follow.

In the PROLOG language, the semantic structure created by the parser

for the phrase "the big red bus" may be represented by:

[

X^Λdet(defs,_.third),X^Λe(bus,[x,x]),

X^ΛVl^Λr(has Value, [a,x]), Vl^Λe(red,[]),

X^ΛV2^Λr(has Value,[a]), V2^Ae (big,[])

]

It will be noted that the four words are linked by a relationship

variable X. It will further be noted that the term for the entity "bus" contained

a list including two entries. This indicates that two modifiers (i.e. adjectives

in this case) have been attached to the noun "bus". This explicit indication of

the number of modifiers attached allows the generator in each target language

only to generate those structures which contain the necessary number of

modifiers, thus reducing the processing time required.

The term "big" is unified, by the variable V2, with a relationship term

which is unified, by the variable X, with "the bus". The relationship term

indicates that the bus has an attribute, the value of which is "big". It is similarly linked by a relationship to the entity term for "red". It

will be noted that the relationship terms linking the entity "bus" to the entities

"big" and "red" differ; the additional "x" in the relationship term linking

"bus" with "red" indicates that this is the second occurring (in the input text)

of the two modifiers, and that an additional modifier "big" has yet to the

attached.

Thus, in this embodiment the parser records the number and order of

occurrence in the input text of multiple modifiers of an entity. Thus, the

generator in each target language is able to reconstruct the translated

equivalent of "the big red bus" preserving the number of modifiers, and

putting them in an appropriate order of occurrence to achieve, in the target

language, the same effect as their original order in the source language input

text.

The significance of the term "a" in the two relationship terms above

will now be discussed with reference to Figure 1 1.

Many modifiers (words or clauses) can occur in different positions; for

example, before or after the noun which they modify. In French, for example,

the normal position of an adjective is after the noun which it modifies. An

adjective may be employed before the noun which it modifies, which usually

indicates that the modifier is being stressed, and gives it more subjective

importance. For example, the phrase "un homme grand" in French has a

different meaning to the phrase "un grand homme". In the parser according to the present invention, for each modifier, in

languages where that modifier can have multiple positions with differing

emphases, at least a "normal" and a "stressed" positions are defined. The

parser is arranged to detect the position of occurrence of a modifier in input

text, relative to the entity (e.g. noun or verb) which it modifies, and to record

this within the relationship terms making up the semantic structure it

produces. In this embodiment, the fact that the modifier is in its normal

position is recorded by the term "a", and a stressed position is recorded by the

term "b".

Thus, in Figure 1 1a, the phrase "there is a swing in the park" is

shown; term Cl refers to the event "there is"; term C2 refers to the phrase "a

swing" and phrase C3 refers to the adverbial phrase "in the park". This

adverbial phrase is in its default or unstressed position, for sentences of this

type, following the noun "swing".

On the other hand, in Figure l ib, the phrase "in the park there is a

swing" is illustrated; in this case, the adverbial phrase comes first, with the

emphasis thereby being shifted away from the location of the swing and

towards the existence of the swing.

Accordingly, these two sentences (which would otherwise give rise to

identical entity/relationship semantic structures as shown in Figure 8) are

distinguished by the value of the default position argument, which forms part

of the relationship term by which the modifier is linked to the noun or other

entity which it modifies. This argument is "a" in Figure 11 a (to indicate the default position of the modifier) and "b" in Figure l ib (to indicate the

stressed position).

The generator in each target language is therefore able, where the

target language also includes a mechanism for putting stress on modifiers, to

place the modifier in the stressed position in the target language where

necessary, even though this position may be different to its position in the

source language.

The target language generator may, where the target language

indicates stress other than by position (for example by inflection), use the

stressed location argument to correspondingly translate the modifier. For

example, the French word "grand" may be translated in English as "great" if

placed in one position or "tall" if placed in another. The manner in which the

stressed position information is utilised will therefore vary with target

language.

Having thus parsed the text (step 410), the abstractor 223 then

accesses the abstracting rules database 228 to locate those source language

phrases which may give rise to translation difficulties. The abstraction

process is recursive, insofar as once one abstraction rule has been applied to

the parsed text, the entire set of abstraction rules is referred to again when

processing the partially abstracted text to identify another abstraction rule to

be applied, repetitively until none of the abstraction rules in the set can be

applied. According to this embodiment, a first category of abstraction rules

relate to the use of verb forms, which are often particularly difficult to

translate. For example, English is unusual in that non-stative verbs (i.e. those

which do not indicate the state of something, such as the verb "to go") are

represented by the progressive form (e.g. "I am going", rather than "I go").

Accordingly, one abstraction rule in the English language abstraction

rules database indicates that a progressive verb form should be replaced by its

non-progressive equivalent (e.g. "I go"), for consistency with other languages

in which that form is more normally used.

Likewise, in French, the reflexive form of the verb is often used in

situations which have little inherently reflexive character. Thus, for example,

the French verb "s' asseoire" is processed by an abstraction rule which

replaces "I sit myself, for example, with "I sit" (the non-reflexive form).

Other rules, rather than operating on all verb forms, detect specific

semantic substructures corresponding to source language idiom phrases within

the input text . There is in the present embodiment, for example, a rule to

detect occurrence of the semantic substructure corresponding to "my name is"

and indicate a more general form (corresponding to "Ich heiβe" in German),

which is more directly translatable.

Some rules within the abstraction rules database are associated with

conditional tests, so that the rule is only valid if the conditional test is met.

The abstraction rule for French reflexive verbs written in the

PROLOG language is: abstract(

[E^Λevent(sit_refl,Vform,Tense,Proj),E^ΛA^Λr(agent,J,A^Λreflex(_P,_N,_G)],

[E^Λevent(sit,Vform,Tense,Proj),E^ΛA^Λr(agent,_)]

)^•

In this rule, the second line is the abstracted, or interlingual form of

structure for the first line (which indicates the reflexive form of the verb "to

sit").

Likewise, a rule for abstracting the present tense of an English verb

would be:

abstract(

[E^Λaux(be,fιn,pres),E^Λevent,presp,_Tense,Proj)],

[E^Λevent(Event,pres,prog,Proj)],

\+ (Event ako stative)

This rule determines whether the verb is stative or not (the third line of

the rule defines the conditional test) and, if not, substitutes the simple present

tense (e.g. "I go") form for the progressive (e.g. "I am going") form.

Thus, in step 412, the abstractor 223 tests each structure generated by

the parser, and where one or more of the abstraction rules is applicable,

converts the detected structure to the alternative form recorded within the rule.

As explained, this test is recursive such that the same rule may be applied at

different stages of an abstraction process in which a structure generated by the

parser is converted to the interlingual structure. After operation of the abstractor, the ideal result should be a single,

complete interlingual structure. If the structure is incomplete (that is to say, it

was not possible to relate together all the words using the grammar and the

abstraction rules) then successful translation will not be possible. If more

than one possible structure is produced, then the input text is considered

ambiguous since it could result in more than one possible translation in at

least one of the target languages. If either of these conditions is met (step

414), a query is transmitted to the user (step 406).

In greater detail, the problematic points within the semantic structure,

corresponding to incomplete or ambiguous meanings, are located, and the

portions of the input text relating to these are formulated into a message and

transmitted back to the user for display and response by the applet 116, with a

query text which may for example say "the following text has not been

understood/is ambiguous."

In a preferred version of the present embodiment, the de-abstractor and

generator 224, 225 corresponding to the input (source) language are employed

(as described in greater detail below) to generate a source language text for

each possible semantic structure where two or more exist, and the query also

includes these texts, prefixed with a statement "one of the following meanings

may be intended, please indicate which is applicable:"

In this case, the message transmitted to the user in step 406 comprises

a form, with control areas which may be selected by the user at the client terminal 100a to indicate an intended meaning for the ambiguous words or

phrases detected within the input text.

In other embodiments, the translator may also include additional

knowledge on the meanings of the words used, which will permit some

possible semantic structures to be rejected as implausible.

For example, in Japanese, where different counting systems are used

to represent different types of object, the entry in the lexical database for each

object may indicate what kind of object it is (person thing and so on) and the

translator will thereby be able to reject structures which count in the wrong

arithmetic for the type of object concerned.

If no such ambiguities are detected, or after all such ambiguities are

resolved (step 414), the single, unified, interlingual semantic structure

produced by the abstractor 223 is then passed to the target language de-

abstractor 234 for the or each target language into which the text is to be

translated. The de-abstractor 234 accesses the abstracting rules database 238

and, on detection of any of the substituted forms (for example "I sit")

substitutes the normal form for the target language (in this case, "I sit myself

in French or "I am sitting" in English). The de-abstracted structure is then

more idiomatically correct in the target language than was the semantic

structure produced by the parser.

Next, in step 418, the target language generator program 235 accesses

the target language grammar rule database 237 and the lexical database 240 and operates upon the de-abstracted semantic structure to generate output

target language text.

The operation of the generator is essentially the reverse of that of the

parser; briefly stated, it operates a chart-parsing algorithm (of a type known of

itself) to take the components of the target language semantic structure

generated by the de-abstractor, look up the applicable rules in the target

language rules database 237, and assemble the corresponding words located

from the lexical database 240 into a string of text ordered in accordance with

the grammar rules, until a single stream of text which utilises all components

of the semantic structure and obeys the grammatical rules is located.

On encountering a noun or other entity with other multiple modifiers,

as noted above, the relevant entry in the lexical database 240 for each modifier

is consulted to determine its default and stress positions, and each modifier is

placed in the appropriate position. Where multiple modifiers are present in

order, they are reproduced in an order appropriate to the target language, using

the stored order data recorded by the parser. During iteration no structures

which do not use all modifiers are generated.

After generating the output text stream, the text is post processed (step

420) to add a space before each word; capitalise the first letter in a sentence;

add a full stop after the last word; contract any phrases (such as "je ai") which

are capable of contraction; and reproduce any special forms of text (such as

dates, amounts of money, and personal names), as appropriate for the target

language. The resulting formatted text is then formulated into an HTML page,

which is transmitted back to the user at the client terminal 100a in step 422.

On receipt of the translation result at the client terminal 100a, the page

is displayed via the browser 114 and may be converted and stored for

subsequent word-processing by the user.

Second Embodiment

In the embodiment described above, the text for translation was

returned to the user. In this embodiment, a multilingual communications

system is provided.

Referring to Figure 12, the communications system comprises a client

terminal 100a similar to the terminal 100 of the first embodiment, connected

to a server 500 (either directly or via a communications network as in the first

embodiment). The server 500 is then interconnected via the network 300 such

as the PSTN or the Internet to a plurality of destination terminals 600, 700,

800.

Thus, in this embodiment, the client terminal 100a does not need, and

does not have, the facility to receive the translated text itself.

In this embodiment, as shown in Figure 13, the server 500 now

contains the text pre-processor 221 , source language parser 222, and source

language abstractor 223, together with operating system and server program

components 212, 214 as before (not shown), but does not contain the

abstractor, generator or text post processor elements for any of the target

languages. Instead, these are present in each of the destination terminals 600, 700, 800 (only 600 is shown in Figure 13). Also present in the server 500 is a

communications circuit 502 and associated control program for transmitting

e-mail messages, and in each of the destination terminals 600-800 a

corresponding communications circuit (e.g. 602) and associated control

program for receiving e-mail messages is provided.

In operation, this embodiment works as described above in relation to

the first, but with the following modifications.

It is envisaged that the target language software would be widely

distributed (for example, available for downloading free) and that the target

language terminals would be personal computers or workstations. On the

other hand, access to the source language components would be controlled;

for example, by restricted access to the server 500 with payment mechanism.

Initially, instead of merely specifying the target language or languages

in which the translation is to be supplied, the user of the client terminal 100a

would be requested to supply e-mail addresses and target languages of the

intended recipients of the translated text. This is conveniently achieved by

arranging for the server to transmit an HTML form to the client terminal 100a

with spaces for the relevant entries.

The client 100a and server 500 then perform steps 402-414 of the

process of Figure 9, with dialogue if necessary between the server and the user

via the client terminal 100a to clarify any ambiguities in the input text. Upon

completion of these steps, the resulting abstracted (interlingual) semantic

structure is then transmitted as an e-mail message to each of the destination terminals 600-800. The volume of data to represent the abstracted

(interlingual) semantic structure is found to correspond approximately to that

of the source text.

Each of the destination terminals 600-800 then performs steps 416-420

of Figure 9 to generate output text files, which are then stored as a received e-

mail message, for subsequent processing or reading.

To give an example, a multinational company may wish to

communicate with a group employees or contractors in Japan, France and

Russia from the UK. The UK author specifies the e-mail addresses and the

three languages Japanese, French and Russian as target languages, and types

in input text in English at the terminal 100a, which is abstracted at server 500.

The interlingual structure is transmitted to destination terminal 600 in France,

destination terminal 700 in Japan and destination terminal 800 in Russia.

Destination terminal 600 has a target language de-abstractor, generator

and post-processor software for French; terminal 700 for Japanese and

terminal 800 for Russian. The received e-mail message at each of these

destination terminals is regenerated into the relevant target language, to enable

each user to review its contents in his own language.

Since it is only necessary to transmit a single message with the same

content to each of the destination terminals, where there are a large number of

recipients it is possible to send one message across long distances and then

distribute it locally at the destination end, thus reducing long distance traffic over the situation where a separately translated copy of the message was sent

to each different recipient.

Transmission is also simplified since it is not necessary to sort

messages in different languages by their recipient, as the data transmitted is

the same for each recipient.

Finally, no matter how many languages are added to the system, the

server 500 and each destination terminal 600-800 do not need to store

multiple language sets of parser/abstractor/de-abstractor/generator software

for every possible language, as is the case in the first embodiment; it is only

necessary for each to store software relating to a single language. Further, the

volume of data occupied by the lexical database 240 may be reduced by

holding, for each client terminal 100a, only the source language records for

each entry and not the target language entries. The effect of increasing the

number of languages is merely to (somewhat) increase the number of the

abstraction rules.

It therefore becomes possible, in this embodiment, to merge the

functions of the server 500 and client terminal 100a, permitting the functions

of the server 500 to be performed on a terminal 100a comprising a personal

computer or workstation, for example.

It will be seen that the various improvements described herein

contribute to providing a translation system suited to modern

telecommunications in particular, since in some aspects the above

embodiments reduce the amount of interaction needed by a user, whilst in other embodiments the processing time and resources required are reduced,

and in yet other embodiments the quality of the translation is improved, whilst

maintaining the possibility for translation into more than one (and preferably a

large number) of target languages.

Although the above embodiments accept a text document, a speech

recognition front-end is also possible, or an image scanner with optical

character recognition could be employed.

Although the above described embodiments describe a translation

system, in which the target language text is generated, it will be understood

that it would be possible with advantage to utilise the interlingual language

structure generated for other purposes; for example, to provide a natural

language front end or input routine for control of a computer or other

equipment. Accordingly, such other uses of some aspects of the invention are

not excluded.

Although adaptation to the intended target languages by limiting the

search within the lexical database 240 to those words occurring in the source

and those target languages has been described, it will be realised that it would

also be possible to limit the operation of the abstractor, and merely to utilise

those abstraction rules which remove language dependency in the source

language which is not also present in the intended target languages.

In this case, each abstraction rule would similarly include a reference

to those languages for which it was necessary, and only the necessary rules for the intended target language(s) would be used. Such an embodiment may

prove useful as the number of target languages increases.

The foregoing embodiments are merely examples of the

invention and are not intended to be limiting, it being understood that many

other alternatives and variants are possible within the scope of the invention.

Protection is sought for any and all novel subject matter disclosed herein and

combinations of such subject matter.

Claims

1. Apparatus for use within a system for translating a document from a

source language to at least one target language, comprising:

means for parsing said document using grammar rules specific to said

source language, to derive a source language-specific semantic structure;

means for processing said source language-specific semantic structure,

to detect the presence therein of semantic elements which are specific to said

source language, and to replace said semantic elements with replacement

elements which are generic to a plurality of possible said target languages, to

generate a generic semantic representation of said document.

2. Apparatus according to claim 1 , wherein said source-language specific

processing means applies a set of processing rules recursively, until no more

rules of said set are applicable.

3. Apparatus according to claim 2, wherein said generic semantic

representation is generated such that each semantic structure in said

representation corresponds directly with a target-language specific semantic

structure of said at least one target language.

4. Apparatus according to claim 3, wherein said system is for translating

a document into any of a predetermined set of target languages and said

generic semantic representation takes into account all of said target languages.

5. Apparatus according to claim 3, wherein said system is for translating

a document into any of a predetermined set of target languages, comprising

means for selecting one or more of said set of target languages and wherein

said generic semantic representation takes into account only said selected

target language(s).

6. Apparatus according to any preceding claim, arranged to identify a

plurality of alternative generic semantic structures when generating said

generic semantic representation, said plurality of alternatives corresponding to

a single semantic structure in said document which can be translated into said

at least one target language in a plurality of different ways, to query a user to

select one of said alternative generic semantic structures, and to include the

selected generic semantic representation in said generic semantic

representation.

7. Apparatus according to claim 6, wherein said single semantic structure

is a lexical item, and said identifying step is conducted by reference to a store

of corresponding lexical items in said at least one target language.

8. Apparatus for use within a system for translating a document from a

source language to at least one target language, comprising:

means for receiving a generic semantic representation of the document

and for processing said generic semantic representation to detect the presence

therein of generic semantic elements which correspond to semantic elements

which are specific to said target language and to replace such elements with

replacement elements specific to said target language to generate a target

language-specific semantic representation; and

means for generating a target language document from said target

language-specific semantic representation.

9. Apparatus according to any preceding claim, arranged to detect or

replace specific semantic elements comprising verb forms.

10. Apparatus according to any preceding claim, arranged to detect or

replace specific semantic elements comprising idiomatic expressions.

11. A system for translating a document from a source language to at least

one target language, comprising apparatus according to any of claims 1 to 7

and apparatus according to claim 8 arranged to receive therefrom said generic

semantic representation.

12. A system according to claim 1 1, in which the apparatus of claim 1 and

the apparatus of claim 2 form a single processor.

13. A system according to claim 12, further comprising a communications

port for receiving said document from a remote terminal via a

telecommunications channel.

14. A system according to claim 13, in which said communications port

carries data using Internet protocols.

15. A system according to claim 13, further comprising said remote

terminal.

16. A system according to claim 15, in which said remote terminal

operates as a hypertext client.

17. A system according to claim 11, in which the apparatus of any of

claims 1 to 7 and the apparatus of claim 8 are at separate sites, and each

comprises communications means for communicating with a

telecommunications channel, the apparatus of any of claims 1 to 7 being

arranged to transmit a said generic representation of said document to one or

more apparatus according to claim 8.

18. A multilingual message transmission system comprising:

a first processor arranged to receive a message in a source language, to

convert said message to an interlingual semantic structure, and to transmit

said structure via a telecommunications channel; and

at least one second terminal separately sited to said first, arranged to

receive the interlingual semantic structure and to convert it into a message in a

target language different to said source language.

19. Parsing apparatus for parsing a document in a source language, in

which modifier linguistic elements which modify other linguistic elements

can be placed in at least first or second positions in the document relative to

those they modify, said first and second positions being interpreted differently

in said source language, comprising:

means for retaining, for linguistic elements of the source language,

information on said first and second positions, and

means for noting the occurrence of said modifier linguistic elements in

said document and recording data indicating whether they are in said first or

second positions relative to those they modify.

20. Apparatus according to claim 19, further comprising means for

recording the relative order, in said document, of multiple said modifier

linguistic elements which modify the same linguistic element.

21. Apparatus according to claim 19 or claim 20, further comprising

means for recording the number, in said document, of multiple said modifier

linguistic elements which modify the same linguistic element.

22. Apparatus according to any of claims 19 to 21, in which said modifier

linguistic elements include one or more of adjectives, adjectival phrases,

adverbs and adverbial phrases.

23. Apparatus for translating a document in a source language into one or

more target languages, comprising parsing apparatus according to any of

claims 19 to 22 and means for generating target language text including

translations of said modifiers in dependence upon said recorded data.

24. Apparatus for use within a system for translating a document from a

source language to at least one target language selected from a multiplicity of

target languages, said source language having a plurality of linguistic

elements which are directly translatable in some but not all of said target

languages, comprising:

means for inputting a user selection of at least one of said multiplicity

of target languages, and

means for converting said document to a generic semantic structure

representative of the document in said source and plural said target languages, said converting means being arranged to detect said linguistic elements in said

document and being capable of converting them into elements translatable in

all of said target languages,

wherein said converting means is responsive to said selection of target

languages, and is arranged not to thus convert those linguistic elements which

are directly translatable in all selected target languages of said multiplicity.

25. Apparatus according to claim 24, wherein said linguistic elements

comprise words, and said converting means comprises a database of said

linguistic elements, including data from which it can be determined which

said words are directly translatable, and the apparatus is arranged to determine

whether each word in the document is directly translatable or not.

26. Apparatus according to claim 24 or claim 25, further comprising

means for generating an output prompting a user to select one of a plurality of

possible options for a linguistic element which is not directly translatable into

one or more of said selected target languages, for receiving a corresponding,

selection input from the user, and for translating said linguistic element in

dependence upon the input.

27. A method of language processing for use within a process for

machine-translating a document from a source language to at least one target

language, comprising: parsing said document using grammar rules specific to said source

language, to derive a source language-specific semantic structure;

processing said source language-specific semantic structure, to detect

the presence therein of semantic elements which are specific to said source

language, and;

replacing said semantic elements with replacement elements which are

generic to a plurality of possible said target languages, to generate a generic

semantic representation of said document

28. A method of language processing for use within a process for

machine-translating a document from a source language to at least one target

language, comprising:

receiving a generic semantic representation of the document;

processing said generic semantic structure to detect the presence

therein of generic semantic elements which correspond to semantic elements

which are specific to said target language;

replacing such elements with replacement elements specific to said_,

target language to generate a target language-specific semantic structure; and

generating a target language document from said target language-

specific semantic structure.

29. A method of multilingual message transmission comprising receiving

a message in a source language; converting said message into an interlingual semantic structure; transmitting said message via a telecommunications

channel to a remote terminal; receiving said interlingual semantic structure

thereat; and converting said interlingual semantic structure into a target

language text.

30. A method of parsing a document in a source language, in which

modifier linguistic elements which modify other linguistic elements can be

placed in at least first or second positions in the document relative to those

they modify, said first and second positions being interpreted differently in

said source language, comprising:

retaining, for linguistic elements of the source language, information

on said first and second positions, and

noting the occurrence of said modifier linguistic elements in said

document and recording data indicating whether they are in said first or

second positions relative to those they modify.

31. A method of translating a document from a source language to at least

one target language selected from a multiplicity of target languages, said

source language having a plurality of linguistic elements which are directly

translatable in some but not all of said target languages, comprising:

inputting a user selection of at least one of said multiplicity of target

languages, converting said document to a generic semantic structure

representative of the document in said source and plural said target languages,

detecting said linguistic elements in said document and converting

them into elements translatable in all of said target languages, responsively to

said selection of target languages, and

not converting those linguistic elements which are directly translatable

in all selected target languages of said multiplicity.