december 2003csa3050: natural language generation 1 what is natural language generation? when is nlg...

December 2003 CSA3050: Natural Language Generation

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CSA3050: Natural Language Generation

What is Natural Language Generation?When is NLG an Appropriate Technology?NLG System Architectures


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Acknowledgements & Resources

• Ehud Reiter and Robert Dale, Building Natural Language Generation Systems, Cambridge:2000.

• SIGGEN's resource page www.dynamicmultimedia.com.au/siggen/

• Dale & Reiter's ANLP-97 Tutorial on Building Applied Natural Language Generation Systems


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Meaning

Text

Natural Language

Understanding

Text

Natural Language

Generation

NLP = NLU + NLG


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What is NLG?• NLG "is the process of deliberately constructing

a natural language text in order to meet specified communicative goals". [McDonald 1992]

• Goal: design of computer software which produces understandable NL utterances.

• Input: some underlying non-linguistic representation of information

• Output: documents, reports, explanations, help messages, and other kinds of texts


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Why Use NLG?

• Important information is often stored on computers in ways which are not comprehensible to the end user:

• NLG systems can present this information to users in an accessible way.

• When output is so variable that is difficult to capture by means of canned text.


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Are NLG and NLU Mirror Images?

• Both Require Knowledge

– knowledge of language

– knowledge of the domain

• Can we use same knowledge to drive NLG and NLU?

• Reversible grammars


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Reversible Grammars are Possible

s --> np, vp.np --> n.vp --> v, np.

n --> [john].n --> [mary].v --> [loves].v --> [hits].


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Reversible Grammar - Output

1 ?- s([john,loves,mary],[]).Yes2 ?- s(X,[]).X = [john, loves, john] ;X = [john, loves, mary] ;X = [john, hits, john] ;X = [john, hits, mary] ;X = [mary, loves, john] ;X = [mary, loves, mary] ;X = [mary, hits, john] ;X = [mary, hits, mary] ;No


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But NLU and NLG AddressFundamentally Different Problems

• NLU– Management of

choices about interpretation.

– Handling ill-formed input.

• NLG– Management of

choices about realisation, given that you know what you want to say.

– Stylistically appropriate output.

– Creating understandable output.


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Deciding what to say involves consideration of ....

• what the content of an utterance should be• what information should be omitted;• how to organise that content in a coherent

discourse;• what tone or degree of formality should be

adopted;• how the material should be broken down into

sentences or clauses;• what syntactic constructions should be used;• how entities should be described;• word choice.


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Examples of Choices

"This course is being taught by Mike Rosner. It is an introduction to natural language generation".

• lecturers name and course title.• style of name• two sentences rather than one.• passive rather than active for first sentence• being taught rather than being given• pronoun it in the second sentence


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Criteria of Understandability/Quality

1. Clear meaning, good grammar, terminology and sentence structure.

2. Clear meaning but bad grammar, bad terminology, or bad sentence structure.

3. Meaning graspable but ambiguities due to bad terminology or bad sentence structure

4. Meaning unclear but inferrable5. Meaning absolutely unclear


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Examples of Understandability/Quality

1. The US unilaterally reduced China's textile export quotas.

2. US cutted china export ration lonely.

3. A chinese ration US cut it down.

4. Cause states go quotas to reduced.

5. alone cut it up rations alone


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When are NLG Techniques Desirable?

• Necessary source data available in a computationally tractable form.

• Much variation in output is required.

• Automation justified on the basis of volume, speed requirements or consistency requirements.

• Text is the right medium.


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Alternatives to/Variations of Natural Language Generation

• Alternatives– Fixed Templates– Templates with Variables– Graphics.– Manual NLG

• Variations– Multi-Modal– Dialogue


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Choice of Text v. Graphics

• No hard and fast rules.• Examination of existing conventions in a

given area of application is useful.• Can depend on type of subject matter, e.g.

– Information about physical location often better conveyed by graphics.

– Information about abstract concepts better conveyed by text.

• Expertise and language abilities of user.


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WIP: Knowledge Based Presentation of Information

• WIP (Wahlster et al c.1990)• Multimodal • Presentation system that is able to generate a variety of

multimedia documents• Input consisting of a formal description of the

communicative intent of a planned presentation.• generation process is controlled by a set of generation

parameters – target group– presentation objective– resource limitation – target language.


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Typical Pipelined Architecture

Text Planning

Sentence Planning

Linguistic Realization


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Tasks and Architecture in NLG

1. Content determination2. Discourse planning (≈ paragraphs)

3. Sentence aggregation4. Lexicalisation

5. Referring expression generation6. Syntax + morphology7. Orthographic realization

Text Planning

Sentence Planning

Linguistic Realizatio

n


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Intermediate Representations

Text Planning

Sentence Planning

Linguistic Realizatio

n

Text Plan

Sentence Plans


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1. Content Determination

• The process of deciding what to say from communicative goals etc.

• construction of a set of messages from the underlying data source– Messages are aggregations of data that are

appropriate for linguistic expression.– Each message may correspond to the

meaning of a word or a phrase.– Messages are based on domain entities,

concepts, and relations.


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Examples of Messages

• DEPARTURETIME(CALEXPRESS, 1000).

• ID(NEXTTRAIN, CALEXPRESS)

• COUNT((TRAIN, SRC(ABERDEEN),

DESTINATION(GLASGOW)), 20, PERDAY)

• The Caledonian Express leaves at 10am

• The next train is the Caledonian Express

• There are 20 trains daily from Aberdeen to Glasgow


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2. Discourse Planning

• A text is not just a random collection of sentences

The Caledonian Express leaves at 10am.The next train is the Caledonian Express.There are 20 trains daily from Aberdeen to Glasgow

• Texts have an underlying structure in which the parts are related together

• The structure can be expressed by means of a text plan


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A Text Plan

NextTrainInformation

IDENTITY(…) DEPARTURETIME(…)

COUNT(…)

Sequence

Elaboration


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Text Resulting from Text Plan

There are 20 trains daily from Aberdeen to Glasgow.The next train is the Caledonian Express.It leaves Aberdeen at 10am.


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3. Sentence Planning:Aggregation

• A one-to-one mapping from messages to sentences results in disfluent text

• Messages need to be combined to produce larger and more complex sentences

• The result is a sentence specification or SENTENCE PLAN


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An Example of Sentence Aggregation

• Without aggregation:– The next train is the Caledonian Express. It leaves Aberdeen at 10am.

• With aggregation:– The next train, which leaves at 10am, is the Caledonian Express.


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4 Lexicalisation

• Lexicalisation determines the particular words to be used to express domain concepts and relations

• In our example, should the DEPARTURETIME relation be expressed using the verb leave or depart?

• How do we express different nuances of meaning?

• What words should be used in different languages?


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5 Referring Expression Generation

• Referring expression generation is concerned with how we describe domain entities in such a way that the hearer will know what we are talking about.

• Choice between– Proper names (type/degree of formality)– Definite Descriptions– Pronouns

• Major issue is avoiding ambiguity.John hit Bill. He cried out.


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6 Syntactic and Morphological Realization

• Morphology: rules of word formation: – walk + ed = walked

• Syntax: rules of sentence formation– the subject goes before the verb


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7 Orthographic Realization

• Orthographic realization is concerned case, punctuation, typographic issues: font size, column width …

• sentences begin with upper case letter, end in full stops

• choice of font

• other layout issues


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Summary• NLG is related to NLU but addresses different

problems.• Quality/understandability is a major issue. • NLG is an option when text is an appropriate

output medium, and when "mail-merge" style character manipulation is insufficient for the application at hand.

• Planning considerations enter into the generation of texts.

• Text generation is a pipeline process involving different representations.

december 2003csa3050: natural language generation 1 what is natural language generation? when is nlg...

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