Computational Pragmatics Michael Franke 2 The biggest collection - - PowerPoint PPT Presentation

Computational Pragmatics

Michael Franke

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The biggest collection of paper clips is in Spaarnwoude, Nordholland.

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Whiskey!

Two views of language

structure function

ambiguous disambiguated by pragmatic reasoning

Semantics

logical

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Frege

On Sense & Reference

“robot”

expression sense

HAS A DETERMINES

reference

Sense (Sinn) ::: intension ::: manner of presentation (Art des Gegebenseins) ::: way of singling out parts in a world to which the expression applies Reference (Bedeutung) ::: extension ::: set of positive instances in a given world / truth-value for saturated

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(1) They got married and had kids. a. They had kids and got married. b. (2) One more ukulele song and I’m out. (3) Jon had no coin or he would have given it to him. (4) If you pour sugar in your coffee, it tastes great. But if you pour sugar and gasoline in your coffee, it tastes awful. (5) A: Do you speak Portuguese? B: My wife does.

Pragmatics

Gricean

“If I say to any one, ‘I saw some of your children to-day’, he might be justified in inferring that I did not see them all, not because the words mean it, but because, if I had seen them all, it is most likely that I should have said so.”

(Mill 1867)

“[O]ne of my avowed aims is to see talking as a special case or variety of purposive, indeed rational, behaviour.”

(Grice 1975)

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(1) They got married and had kids. a. They had kids and got married. b.

L assumes S obeys Maxims by Manner, L assumes S to be “orderly” L expects S to present events in chronological order (unless otherwise indicated)

(5) A: Do you speak Portuguese? B: My wife does.

L assumes S obeys Maxims by Quantity & Relevance, L assumes S to give all the relevant information S is able to if S was able to speak Portuguese, S would/ should have said so

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Levinson Horn Atlas Q-Implicatures (6) I saw some of your children. ↳ I saw some but not all of your children. (7) I saw Jack or Jill. ↳ I don’t know which. (8) Every ten minutes a man gets mugged in New York City. ↳ Not the same poor fellow every time. I-Implicatures (8) Every ten minutes a light blinks on the machine. ↳ The same light every time. (9) Black Bart caused the sherif to die. ↳ In some unusual manner, perhaps by accident. M-Implicatures

language as a formal system interaction & communication

Gricean ideas Neo-Grice

Gazdar, Horn, Atlas, Levinson, Russell, Sauerland, Schulz, van Rooij, Spector,

Post-Gricean

Sperber, Wilson, Carston s y s t e m a t i z a t i

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f

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m a l i z a t i

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r e d u c t i

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e v

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. / c

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n . f

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n d a t i

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embedding in compositional semantics

Grammaticalism

Chierchia, Fox, Spector, Magri meanwhile elsewhere

Game theory

Parikh, Jäger, Benz, van Rooij

Optimality theory

Blutner, Zeevat, Hendriks, de Hoop, Jäger, Mattausch, Aloni, Krifka

Theoretical Economics

rational communication message credibility

iterated reasoning

Benz, van Rooij, Jäger, Franke, Rothschild, Pavan, Stevens

Cognitive Science

probabilistic (Bayesian) modeling

RSA

Pragmatics

probabilistic

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∀

situation situation “I saw some of your children today.”

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literal interpreter rational speaker

“all”

∀

∃¬∀

“some”

∀

∃¬∀

“all”

∀

“some”

rational interpreter

∀

∃¬∀

“all” “some”

rational speaker literal interpreter rational interpreter

rational speaker literal interpreter “all” 1 “some” .5 .5

∀

∃¬∀ rational interpreter “all” 1 “some” 1

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∃¬∀ “all” “some” 1 1

∀

∃¬∀

literal interpreter “all” 1 “some” .5 .5

∀

∃¬∀ rational interpreter “all” .9 .1 “some” .1 .9

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∃¬∀ rational speaker “all” “some” .9 .1 .1 .9

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∃¬∀ approximately

literal interpreter “all” 1 “some” .5 .5

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∃¬∀ rational interpreter “all” .9 .1 “some” .1 .9

∀

∃¬∀ rational speaker “all” “some” .9 .1 .1 .9

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∃¬∀ approximately

listener behavior speaker behavior

U → ∆(S) S → ∆(U)

Rational Speech Act model

L0 S1 L1

PS(m|s) ∝ exp (α (log Plit(s|m)−C(m)))

PL(s|m) ∝ P(s) PS(m|s) Plit(s|m) = P(s ∣ [ [m] ])

literal interpretation Gricean speaker Gricean interpretation

strategic depth 0 strategic depth 1 strategic depth 2

This course

applications technicalities WebPPL Bayesian Data Analysis … referential communication (epistemic) scalar implicatures non-literal language use vagueness politeness …

Reference Games

referential communication

U = {”square”, ”circle”, ”green”, ”blue”}

context

set of objects/referents

utterances

single properties of objects

which object do you think a speaker meant when she selects “blue”?

RSA for reference games (example)

rational speaker literal interpreter

“square” .5 .5 “circle” 1 “green” 1 “blue” .5 .5

rational interpreter

“square” .82 .18 “circle” 1 “green” 1 “blue” .82 .18 “square” “circle” “green” “blue” .5 .5 .89 .11 .11 .89