kees van deemter matthew stone formal issues in natural language generation lecture 4 shieber 1993;...

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Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

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Page 1: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Kees van DeemterMatthew Stone

Formal Issuesin

Natural Language Generation

Lecture 4Shieber 1993; van Deemter

2002

Page 2: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Semantics

Formal semantics concentrates on information content and its representation.

To what extent does good NLG depend on the right information? To what extent does good NLG depend on the right representation?

Note: GRE, but also more general.

Page 3: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

Logical space: all the ways things could turn out to be

Page 4: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

Logical space: all the ways things could turn out to be

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

Page 5: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

A proposition - information

Identifies particular cases as real possibilities

Page 6: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

Here is a particular proposition.

Page 7: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

A wrinkle

Computer systems get their knowledge of logical space,common ground, etc. from statements in formal logic.

Lots of formulas can carry the same information.

Page 8: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

ABC ABC ABC ABC

Page 9: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

AB AB

Page 10: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

(A B) (A B)

Page 11: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

F (A B)

Page 12: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Shieber 1993

The problem of logical form equivalence is about how you get this representation.

In general, an algorithm can choose this representation in one of two ways:In a reasoner that does general, non-

grammatical inference.Using at least some grammatical

knowledge.

Page 13: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Shieber 1993

If it is chosen without access to the grammar (modularly) then the surface realizer has to know what logical formulas mean the same.

This is intractable,philosophically, because the notion

is impossible to pin down andcomputationally, because our best

attempts are not computable.

Page 14: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

What about GRE?

Arguably, GRE uses a grammar.– Parameters such as the preference order on

properties reflect knowledge of how to communicate effectively.

– Decisions about usefulness or completeness of a referring expression reflect beliefs about utterance interpretation.

Maybe this is a good idea for NLG generally.

Page 15: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Letting grammar fix representationChoice of alternatives

reflects linguistic notions – discourse coherence, information structure, function.

ABC ABC ABC ABC

AB AB

(A B) (A B)

F (A B)

Page 16: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Now there’s a new question

If grammar is responsible for how information is represented, where does the information itself come from?

To answer, let’s consider information and communication in more detail.

Page 17: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

Logical space: all the ways things could turn out to be

Page 18: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

Common ground: the possibilities mutual knowledgestill leaves open.

Page 19: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

Common ground: the possibilities mutual knowledgestill leaves open.

Page 20: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

Private knowledge: the things you take as possible.

Page 21: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

Private knowledge: the things you take as possible.

Page 22: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

Communicative Goal: an important distinctionthat should go on the common ground.

Page 23: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

Communicative Goal: an important distinctionthat should go on the common ground.

Page 24: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Formal question

What information satisfies what communicative goals?

Objective: modularitygeneral reasoning gives communicative goals, grammar determines information.

Another meaty issue.

Page 25: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Information in NLG

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

Communicative Goal: an important distinctionthat should go on the common ground.

Page 26: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

What John ate was a piece of fruit.

Page 27: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

John didn’t eat the cake.

Page 28: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

John ate one thing.

Page 29: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

John ate at most one thing.

Page 30: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

For example

John atenothing.

John atethe cake

(C).

John ateB+C.

John ateA+C.

John atethe banana

(B).

John atethe apple

(A).

John ateA+B.

John ateA, B+C.

What John ate was the apple.

Page 31: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Formal questions

What information satisfies what communicative goals?

Let u be the info. in the utterance.Let g be goal info.Let c, p be info. in common ground,

private info.

u = g?p u g?c u = c g?p c u c g?

Page 32: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

Logical form equivalence

An inference problem is inevitableu = g?p u g?c u = c g?p c u c g?

But the problems are very differentnot always as precise (entailment vs.

equivalence)not always as abstract (assumptions, context,

etc.)

Consequences for philosophical & computational tractability.

Page 33: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

GRE, again

We can use GRE to illustrate, assumingc = domain (context set)g = set of individuals to identify

represented as set of discourse refsu = identifying description

represented as a conjunction of properties

solution criterionc u = c g

Page 34: Kees van Deemter Matthew Stone Formal Issues in Natural Language Generation Lecture 4 Shieber 1993; van Deemter 2002

GRE

How does the algorithm choose representation of u?

The algorithm finds a canonical representation of u, based on incremental selection of properties.

And how does the representation and choice of u relate to the representation and choice of an actual utterance to say?

The representation of u works as a sentence plan.