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Formal Issues in Natural Language Generation. Lecture 4 Shieber 1993; van Deemter 2002. Semantics. Formal semantics concentrates on information content and its representation. - PowerPoint PPT Presentation
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Kees van DeemterMatthew Stone
Formal Issuesin
Natural Language Generation
Lecture 4Shieber 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.
Information in NLG
Logical space: all the ways things could turn out to be
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.
A proposition - information
Identifies particular cases as real possibilities
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.
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.
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
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
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)
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)
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.
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.
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.
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)
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.
Information in NLG
Logical space: all the ways things could turn out to be
Information in NLG
Common ground: the possibilities mutual knowledgestill leaves open.
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.
Information in NLG
Private knowledge: the things you take as possible.
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.
Information in NLG
Communicative Goal: an important distinctionthat should go on the common ground.
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.
Formal question
What information satisfies what communicative goals?
Objective: modularitygeneral reasoning gives communicative goals, grammar determines information.
Another meaty issue.
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.
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.
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.
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.
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.
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.
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?
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.
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
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.
