linguistics association of great britain, annual meeting...

Introduction to the Rational Speech Acts Model

Christopher PottsStanford Linguistics

Linguistics Association of Great Britain, Annual MeetingSeptember 9, 2019

Overview

OverviewA companion to Goodman and Frank 2016, ‘Pragmatic language interpretation as probabilistic inference’


1. A bit of history


1. A bit of history2. Conceptual motivation for the Rational Speech Acts model (RSA)


1. A bit of history2. Conceptual motivation for the Rational Speech Acts model (RSA)3. Example calculations using the model:

a. Basic scalar implicatureb. The role of message costsc. The role of the alpha parameterd. The role of the referent prior




4. A simple Python implementation




4. A simple Python implementation5. Extensions: joint inference over utterance and context

Origin story

Origin story1. Rosenberg and Cohen 1964 (Science): early Bayesian model of production

and comprehension


and comprehension

2. Lewis 1969: signaling systems in his thesis/book Convention


and comprehension


3. Rabin 1990: recursive signaling in ‘Communication between rational agents’


and comprehension



4. Camerer and Ho 2004: ‘A cognitive hierarchy models for games’ of conflict and coordination


and comprehension




5. Michael Franke and Gerhard Jäger: iterated best response (IBR)


and comprehension




5. Michael Franke and Gerhard Jäger: iterated best response (IBR)

6. Frank and Goodman 2012 (Science): very sophisticated pragmatic agents and a new Bayesian foundation

Major achievementsIncremental implicatures Cohn-Gordon, Goodman, Potts, ‘A incremental, iterated response model of

pragmatics’

Manner implicatures Bergen, Levy, Goodman, ‘Pragmatic reasoning through semantic inference’

I-implicatures and implicature blocking Potts & Levy, ‘Negotiating lexical uncertainty and speaker expertise with disjunction’

Embedded implicatures Potts, Lassiter, Levy, Frank, ‘Embedded implicatures as pragmatic inferences under compositional lexical uncertainty’

Hyperbole Kao, Wu, Bergen, Goodman, ‘Nonliteral understanding of number words’

Metaphor Kao, Bergen, Goodman, ‘Formalizing the pragmatics of metaphor understanding’

Politeness Yoon, Tessler, Goodman, Frank, ‘Polite speech emerges from competing social goals’

Irony Cohn-Gordon and Bergen, ‘Verbal irony, pretense, and the common ground’

Social meaning Work by E. Allyn Smith, Heather Burnett, Eric Acton, and others

Large-scale machine learning problems Work by Will Monroe, Jacob Andreas, Reuben Cohn-Gordon, and others

Pursuing a Gricean ideal

Pursuing a Gricean idealThe cooperative principle: Make your contribution as is required, when it is required, by the conversation in which you are engaged.

Pursuing a Gricean idealThe cooperative principle: Make your contribution as is required, when it is required, by the conversation in which you are engaged.

1. Quality: Contribute only what you know to be true. Do not say false things. Do not say things for which you lack evidence.

2. Quantity: Make your contribution as informative as is required. Do not say more than is required.

3. Relation (Relevance): Make your contribution relevant.

4. Manner: (i) Avoid obscurity; (ii) avoid ambiguity; (iii) be brief; (iv) be orderly.

5. ...

Conversational implicature

Conversational implicatureProposition q is a conversational implicature of an utterance U if and only if


1. The speaker S believes it is mutual, public knowledge of all discourse participants that S is obeying the cooperative principle.



2. S believes that, to maintain (1) given U, the discourse participants will assume S believes q.




3. S believes it is mutual, public knowledge of all discourse participants that (2) holds.





Alex: What city does Paul live in?

Ryan: Hmm, he lives in California.





Alex: What city does Paul live in?

Ryan: Hmm, he lives in California.

A. Assume Ryan is cooperative.B. Ryan supplied less information that was required,

seemingly contradicting (A).C. Assume Ryan doesn’t know what city Paul lives in = q.D. Then Ryan’s answer is optimal given this evidence.


My friend has glasses.



My listener knows I’m cooperative in the Gricean sense.




So they will be able to work out that I mean “not hat” as well.




The speaker’s utterance seems ambiguous or under-informative.






But I’m assuming the speaker is cooperative in the Gricean sense!






But I’m assuming the speaker is cooperative in the Gricean sense!

Ah, but if I assume they mean to convey “not hat” too, then all’s well!


The Rational Speech Acts Model (RSA)

A simple reference game


r1 r2


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

r1 r2 ⟦⟧


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

r1 0.5

r2 0.5

r1 r2 ⟦⟧ P


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

r1 0.5

r2 0.5

‘hat’ 0

‘glasses’ 0

r1 r2 ⟦⟧ P C


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

r1 0.5

r2 0.5

‘hat’ 0

‘glasses’ 0

r1 r2 ⟦⟧ P C

𝜶 = 1


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

r1 0.5

r2 0.5

‘hat’ 0

‘glasses’ 0

r1 r2 ⟦⟧ P C

𝜶 = 1 r1 r2 C

‘hat’ 0 1 0

‘glasses’ 1 1 0

P 0.5 0.5

𝜶 = 1

A scalar implicature 𝜶 = 1 r1 r2 C

‘hat’ 0 1 0

‘glasses’ 1 1 0

P 0.5 0.5

A scalar implicaturer1 r2

‘hat’ 0 1

‘glasses’ 1 1


‘hat’ 0 1

‘glasses’ 1 1PLit r1 r2

‘hat’ 0 1

‘glasses’ 0.5 0.5


‘hat’ 0 1


‘hat’ 0 1


referents r given messages m


‘hat’ 0 1


‘hat’ 0 1


truth value of message m for referent r



‘hat’ 0 1


‘hat’ 0 1


sum of all truth values of message m over all referents r′

truth value of message m for referent r



‘hat’ 0 1


‘hat’ 0 1


⟦‘hat’⟧(r1)

⟦‘hat’⟧(r1) + ⟦‘hat’⟧(r2)


‘hat’ 0 1


‘hat’ 0 1


⟦‘hat’⟧(r1)

⟦‘hat’⟧(r1) + ⟦‘hat’⟧(r2)

0

0 + 1 =


‘hat’ 0 1


‘hat’ 0 1


⟦‘hat’⟧(r1)

⟦‘hat’⟧(r1) + ⟦‘hat’⟧(r2)

0

0 + 1 = = 0


‘hat’ 0 1


‘hat’ 0 1


⟦‘glasses’⟧(r2)

⟦‘glasses’⟧(r1) + ⟦‘glasses’⟧(r2)


‘hat’ 0 1


‘hat’ 0 1


1

1 + 1 =




‘hat’ 0 1


‘hat’ 0 1


1

1 + 1 = = 0.5




‘hat’ 0 1


‘hat’ 0 1



‘hat’ 0 1


‘hat’ 0 1


r1


‘hat’ 0 1


‘hat’ 0 1


r1

r2


‘hat’ 0 1


‘hat’ 0 1


‘hat’

r1

r2


‘hat’ 0 1


‘hat’ 0 1


‘hat’ ‘glasses’

r1

r2


‘hat’ 0 1


‘hat’ 0 1



r1 0 0.5

r2 1 0.5


‘hat’ 0 1


‘hat’ 0 1


PS ‘hat’ ‘glasses’

r1 0 1

r2 0.67 0.33


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

messages m given referents r


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

messages m given referents r

literal listener, rather than truth conditions


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

PLit(r2 | ‘hat’)

PLit(r2 | ‘hat’) + PLit(r2 | ‘glasses’)


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33



1

1 + 0.5 =


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33



1

1 + 0.5 = = 0.67


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

‘hat’


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

‘hat’

‘glasses’


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

r1

‘hat’

‘glasses’


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

r1 r2

‘hat’

‘glasses’


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

r1 r2

‘hat’ 0 0.67

‘glasses’ 1 0.33


‘hat’ 0 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

PL r1 r2

‘hat’ 0 1

‘glasses’ 0.75 0.25

The calculation in more detail

The calculation in more detailr1 r2

‘hat’ 0 1

‘glasses’ 1 1


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1


Normalize the rows


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5

Normalize the rowsTranspose


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5


r1 0 1

r2 0.67 0.33

Normalize the rowsTranspose

Normalize the rows


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5

r1 r2

‘hat’ 0 0.67



r1 0 1

r2 0.67 0.33

Normalize the rowsTranspose Transpose

Normalize the rows


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5

r1 r2

‘hat’ 0 0.67



r1 0 1

r2 0.67 0.33

PL r1 r2

‘hat’ 0 1

‘glasses’ 0.75 0.25

Normalize the rowsTranspose Transpose

Normalize the rows Normalize the rows

The role of message costs𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0

The role of message costsPLit r1 r2

‘hat’ 0 1


𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0


‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0


‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0

= 1 so we can ignore it for now


‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0

from probabilities to scores


‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0


so we can bring in real-valued costs


‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0



returns us normalizable values


‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0



often written e𝜶(log PLit(r|m) + C(m))



‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0



Note: exp(log(x)) = x, which is why we could leave this out when we didn’t have costs and 𝜶 = 1

often written e𝜶(log PLit(r|m) + C(m))



‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0

exp(log(PLit(r2 | ‘hat’) + C(‘hat’)))

exp(log(PLit(r2 | ‘hat’) + C(‘hat’))) + exp(log(PLit(r2 | ‘glasses’) + C(‘glasses’)))


‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0

exp(log(1) – 6)

exp(log(1) – 6) + exp(log(0.5) – 0)=

exp(log(PLit(r2 | ‘hat’) + C(‘hat’)))

exp(log(PLit(r2 | ‘hat’) + C(‘hat’))) + exp(log(PLit(r2 | ‘glasses’) + C(‘glasses’)))


‘hat’ 0 1



r1 0 1

r2 0.0049 0.9951

PL r1 r2

‘hat’ 0 1

‘glasses’ 0.5012 0.4988

𝜶 = 1 r1 r2 C

‘hat’ 0 1 –6

‘glasses’ 1 1 0

The calculation with costs in more detail

The calculation with costs in more detailr1 r2

‘hat’ 0 1

‘glasses’ 1 1


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1


Normalize


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5

Transpose


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5


r1 exp(log(0) – 6) exp(log(0.5) – 0)

r2 exp(log(1) – 6) exp(log(0.5) – 0)

Add costs


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5


r1 exp(log(0) – 6) exp(log(0.5) – 0)

r2 exp(log(1) – 6) exp(log(0.5) – 0)


r1 0 0.5

r2 0.0025 0.5


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5


r1 0 0.5

r2 0.0025 0.5


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5


r1 0 1

r2 0.0049 0.9951


r1 0 0.5

r2 0.0025 0.5

Normalize


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5


r1 0 1

r2 0.0049 0.9951


r1 0 0.5

r2 0.0025 0.5

r1 r2

‘hat’ 0 0.0049

‘glasses’ 1 0.9951

Transpose


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5


r1 0 1

r2 0.0049 0.9951


r1 0 0.5

r2 0.0025 0.5

r1 r2

‘hat’ 0 0.0049

‘glasses’ 1 0.9951

PL r1 r2

‘hat’ 0 1

‘glasses’ 0.5012 0.4988

Normalize


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.5

r2 1 0.5


r1 0 1

r2 0.0049 0.9951


r1 0 0.5

r2 0.0025 0.5

r1 r2

‘hat’ 0 0.0049

‘glasses’ 1 0.9951

PL r1 r2

‘hat’ 0 1

‘glasses’ 0.5012 0.4988

The role of the alpha parameter

The role of the alpha parameterPLit r1 r2

‘hat’ 0 1


r1 r2

‘hat’ 0 1

‘glasses’ 1 1


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

r1 r2

‘hat’ 0 1

‘glasses’ 1 1

𝜶 = 1.0


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

r1 r2

‘hat’ 0 1

‘glasses’ 1 1


r1 0 1

r2 0.94 0.06

𝜶 = 1.0

𝜶 = 4.0


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

r1 r2

‘hat’ 0 1

‘glasses’ 1 1


r1 0 1

r2 0.94 0.06

𝜶 = 1.0

𝜶 = 4.0

exp(1 * log(0.75)) = 3 * exp(1 * log(0.25))


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

r1 r2

‘hat’ 0 1

‘glasses’ 1 1


r1 0 1

r2 0.94 0.06

𝜶 = 1.0

𝜶 = 4.0

exp(1 * log(0.75)) = 3 * exp(1 * log(0.25))

exp(4 * log(0.75)) = 81 * exp(4 * log(0.25))


‘hat’ 0 1



r1 0 1

r2 0.67 0.33

r1 r2

‘hat’ 0 1

‘glasses’ 1 1


r1 0 1

r2 0.94 0.06

𝜶 = 1.0

𝜶 = 4.0 Higher 𝜶 means stronger pragmatic inferences

exp(1 * log(0.75)) = 3 * exp(1 * log(0.25))

exp(4 * log(0.75)) = 81 * exp(4 * log(0.25))

The role of referent priorr1 r2

‘hat’ 0 1

‘glasses’ 1 1

P 0.3 0.7

The role of referent priorPLit r1 r2

‘hat’ 0 1


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

P 0.3 0.7


‘hat’ 0 1


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

P 0.3 0.7

⟦‘glasses’⟧(r2) * P(r2)

⟦‘glasses’⟧(r1) * P(r1) + ⟦‘glasses’⟧(r2) * P(r2)


‘hat’ 0 1


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

P 0.3 0.7

1*0.7

1*0.3 + 1*0.7 = = 0.7

⟦‘glasses’⟧(r2) * P(r2)

⟦‘glasses’⟧(r1) * P(r1) + ⟦‘glasses’⟧(r2) * P(r2)


‘hat’ 0 1


r1 r2

‘hat’ 0 1

‘glasses’ 1 1

P 0.3 0.7


‘hat’ 0 1



r1 0 1

r2 0.59 0.41

r1 r2

‘hat’ 0 1

‘glasses’ 1 1

P 0.3 0.7


‘hat’ 0 1



r1 0 1

r2 0.59 0.41

PL r1 r2

‘hat’ 0 1

‘glasses’ 0.51 0.49

r1 r2

‘hat’ 0 1

‘glasses’ 1 1

P 0.3 0.7

The calculation with referent prior in more detail

The calculation with referent prior in more detailr1 r2

‘hat’ 0 1

‘glasses’ 1 1


‘hat’ 0 1

‘glasses’ 1 1

r1 r2

‘hat’ 0*0.3 1*0.7

‘glasses’ 1*0.3 1*0.7

Incorporate priors


‘hat’ 0 1

‘glasses’ 1 1

r1 r2

‘hat’ 0*0.3 1*0.7

‘glasses’ 1*0.3 1*0.7

r1 r2

‘hat’ 0 0.7



‘hat’ 0 1

‘glasses’ 1 1

r1 r2

‘hat’ 0 0.7



‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1


r1 r2

‘hat’ 0 0.7


Normalize


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.3

r2 1 0.7

r1 r2

‘hat’ 0 0.7

‘glasses’ 0.3 0.7 Transpose


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.3

r2 1 0.7


r1 0 1

r2 0.59 0.41

r1 r2

‘hat’ 0 0.7


Normalize


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.3

r2 1 0.7

r1 r2

‘hat’ 0 0.59



r1 0 1

r2 0.59 0.41

r1 r2

‘hat’ 0 0.7

‘glasses’ 0.3 0.7 Transpose


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.3

r2 1 0.7

r1 r2

‘hat’ 0 0.59



r1 0 1

r2 0.59 0.41

r1 r2

‘hat’ 0 0.7


r1 r2

‘hat’ 0*0.3 0.59*0.7

‘glasses’ 1*0.3 0.41*0.7

Incorporate priors


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.3

r2 1 0.7

r1 r2

‘hat’ 0 0.59



r1 0 1

r2 0.59 0.41

r1 r2

‘hat’ 0 0.7


r1 r2

‘hat’ 0 0.413

‘glasses’ 0.3 0.287


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.3

r2 1 0.7

r1 r2

‘hat’ 0 0.59



r1 0 1

r2 0.59 0.41

PL r1 r2

‘hat’ 0 1

‘glasses’ 0.51 0.49

r1 r2

‘hat’ 0 0.7


r1 r2

‘hat’ 0 0.413

‘glasses’ 0.3 0.287

Normalize


‘hat’ 0 1

‘glasses’ 1 1

PLit r1 r2

‘hat’ 0 1



r1 0 0.3

r2 1 0.7

r1 r2

‘hat’ 0 0.59



r1 0 1

r2 0.59 0.41

PL r1 r2

‘hat’ 0 1

‘glasses’ 0.51 0.49

r1 r2

‘hat’ 0 0.7


r1 r2

‘hat’ 0 0.413

‘glasses’ 0.3 0.287

A simple Python implementation

http://web.stanford.edu/class/linguist130a/

Major achievementsIncremental implicatures Cohn-Gordon, Goodman, Potts, ‘A incremental, iterated response model of

pragmatics’

Manner implicatures Bergen, Levy, Goodman, ‘Pragmatic reasoning through semantic inference’

I-implicatures and implicature blocking Potts & Levy, ‘Negotiating lexical uncertainty and speaker expertise with disjunction’

Embedded implicatures Potts, Lassiter, Levy, Frank, ‘Embedded implicatures as pragmatic inferences under compositional lexical uncertainty’

Hyperbole Kao, Wu, Bergen, Goodman, ‘Nonliteral understanding of number words’

Metaphor Kao, Bergen, Goodman, ‘Formalizing the pragmatics of metaphor understanding’

Politeness Yoon, Tessler, Goodman, Frank, ‘Polite speech emerges from competing social goals’

Irony Cohn-Gordon and Bergen, ‘Verbal irony, pretense, and the common ground’



Manner implicatures Stop the car tends to signal a normal event.

Cause the car to stop tends to signal an unusual event.

Joint inference over utterance and lexicon

Our uncertain lexicons1. synagogues and other churches

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches3. You may need angioplasty or surgery.

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches3. You may need angioplasty or surgery.4. He’s a wine lover, or oenophile.

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches3. You may need angioplasty or surgery.4. He’s a wine lover, or oenophile.5. superb but not outstanding

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches3. You may need angioplasty or surgery.4. He’s a wine lover, or oenophile.5. superb but not outstanding6. outstanding but not superb

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches3. You may need angioplasty or surgery.4. He’s a wine lover, or oenophile.5. superb but not outstanding6. outstanding but not superb7. It’s a couch, not a sofa.

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches3. You may need angioplasty or surgery.4. He’s a wine lover, or oenophile.5. superb but not outstanding6. outstanding but not superb7. It’s a couch, not a sofa.8. Does between 5 and 10 include 5 and 10?

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches3. You may need angioplasty or surgery.4. He’s a wine lover, or oenophile.5. superb but not outstanding6. outstanding but not superb7. It’s a couch, not a sofa.8. Does between 5 and 10 include 5 and 10?9. Is a barbecue a machine?

Our uncertain lexicons1. synagogues and other churches2. synagogues or churches3. You may need angioplasty or surgery.4. He’s a wine lover, or oenophile.5. superb but not outstanding6. outstanding but not superb7. It’s a couch, not a sofa.8. Does between 5 and 10 include 5 and 10?9. Is a barbecue a machine?

10. Can a horse be an athlete?

Joint inference over utterance and something else

Joint inference over utterance and something elseIncremental implicatures Cohn-Gordon, Goodman, Potts, ‘A incremental, iterated response model of

pragmatics’

Manner implicatures Listener inference over the lexicon

I-implicatures and implicature blocking Listener inference over the lexicon; speaker communication about the lexicon

Embedded implicatures Listener inference over the lexicon

Hyperbole Listener inference over a social/emotional goal

Metaphor Listener inference over the question under discussion

Politeness Speaker pursuing social and presentational goals

Irony Speaker communicating about the presumed common ground



Wrapping up

Wrapping up1. My primary goal was to motivate RSA and walk you through some example

calculations.


calculations.

2. My hope is that this leads you to think about joint inference versions of RSA that can model intricate pragmatic phenomena.


calculations.


3. Additional materials:

a. YouTube video of the core calculations: https://youtu.be/bPd6CNy5UqA

b. Python implementation: https://web.stanford.edu/class/linguist130a/materials/rsa130a.py

c. https://probmods.org

https://youtu.be/bPd6CNy5UqA

https://web.stanford.edu/class/linguist130a/materials/rsa130a.py

https://probmods.org


calculations.


3. Additional materials:

a. YouTube video of the core calculations: https://youtu.be/bPd6CNy5UqA

b. Python implementation: https://web.stanford.edu/class/linguist130a/materials/rsa130a.py

c. https://probmods.org

Thanks!

https://youtu.be/bPd6CNy5UqA

https://web.stanford.edu/class/linguist130a/materials/rsa130a.py

https://probmods.org

linguistics association of great britain, annual meeting...

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