Conversational implicatures: interacting with grammar Christopher - - PowerPoint PPT Presentation

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Conversational implicatures: interacting with grammar

Christopher Potts

Stanford Linguistics

UIUC Linguistics, October 28, 2013

This talk: partly joint work with Mike Frank, Noah Goodman, Dan Jurafsky, Roger Levy & Adam Vogel Associated paper (draft form; comments welcome!): http://stanford.edu/˜cgpotts/papers.html

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Conversational implicature

Definition (Grice 1975)

Speaker S saying U to listener L conversationally implicates q iff

1 S and L mutually, publicly presume that S is cooperative. 2 To maintain 1 given U, it must be supposed that S thinks q. 3 S thinks that both S and L mutually, publicly presume that L is

willing and able to work out that

2 holds.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Conversational implicature

Definition (Grice 1975)

Speaker S saying U to listener L conversationally implicates q iff

1 S and L mutually, publicly presume that S is cooperative. 2 To maintain 1 given U, it must be supposed that S thinks q. 3 S thinks that both S and L mutually, publicly presume that L is

willing and able to work out that

2 holds.

Example

Ann: What city does Paul live in? Bob: Hmm . . . he lives in California. (A) Assume Bob is cooperative. (B) Bob supplied less information than was required, seemingly contradicting (A). (C) Assume Bob does not know which city Paul lives in. (D) Then Bob’s answer is optimal given his evidence.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Conversational implicature

Definition (Grice 1975)

Speaker S saying U to listener L conversationally implicates q iff

1 S and L mutually, publicly presume that S is cooperative. 2 To maintain 1 given U, it must be supposed that S thinks q. 3 S thinks that both S and L mutually, publicly presume that L is

willing and able to work out that

2 holds.

Implicature as social, interactional

Implicatures are inferences that listeners make to reconcile the speaker’s linguistic behavior with the assumption that the speaker is cooperative.

Implicatures and cognitive complexity

The speaker must believe that the listener will infer that the speaker believes the implicature.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Two strands of inquiry

Interactional models

• Embrace the social nature of implicatures.
• Derive implicatures from nested belief models with

cooperative structure.

• Focus on contextual variability and uncertainty.

Grammar models

• Limit interaction to semantic interpretation.
• Derive implicatures without nested beliefs or cooperativity.
• Place variability and uncertainty outside the theory of

implicature.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Two strands of inquiry

Interactional models

• Embrace the social nature of implicatures.
• Derive implicatures from nested belief models with

cooperative structure.

• Focus on contextual variability and uncertainty.

Grammar models

• Limit interaction to semantic interpretation.
• Derive implicatures without nested beliefs or cooperativity.
• Place variability and uncertainty outside the theory of

implicature.

My goal

Despite divisive rhetoric, the two sides in this debate are not in

• pposition, but rather offer complementary insights.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Plan for today

1 Conversational implicature 2 Interactional models of implicature 3 Grammar-driven models of implicature 4 Embedded implicatures 5 Uncancelable implicatures

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

(Scalar) Implicature calculation

Example

A: Sandy’s work this term was satisfactory. Implicature: Sandy’s work was not excellent (= ¬q)

1 Contextual premise: the speaker A intends to exhaustively

answer ‘What was the quality of Sandy’s work this term?’

2 Contextual premise: A has complete knowledge of Sandy’s

work for the term (say, A assigned all the grades for the class).

3 Assume A is cooperative in the Gricean sense. 4 The proposition q that Sandy’s work was excellent is more

informative than p, the content of A’s utterance.

5 q is as polite and easy to express in this context as p. 6 By 1 , q is more relevant than p. 7 By 3 – 6 , A must lack sufficient evidence to assert q. 8 By 2 , A must lack evidence for q because q is false.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

(Scalar) Implicature calculation

Example

A: Sandy’s work this term was satisfactory. Implicature: Sandy’s work was not excellent (= ¬q)

1 Contextual premise: the speaker A intends to exhaustively

answer ‘What was the quality of Sandy’s work this term?’

2 Contextual premise: A has complete knowledge of Sandy’s

work for the term (say, A assigned all the grades for the class).

3 Assume A is cooperative in the Gricean sense. 4 The proposition q that Sandy’s work was excellent is more

informative than p, the content of A’s utterance.

5 q is as polite and easy to express in this context as p. 6 By 1 , q is more relevant than p. 7 By 3 – 6 , A must lack sufficient evidence to assert q. 8 By 2 , A must lack evidence for q because q is false.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

(Scalar) Implicature calculation

Example

A: Sandy’s work this term was satisfactory. Implicature: Sandy’s work was not excellent (= ¬q)

1 Contextual premise: the speaker A intends to exhaustively

answer ‘What was the quality of Sandy’s work this term?’

2 Contextual premise: A has complete knowledge of Sandy’s

work for the term (say, A assigned all the grades for the class).

3 Assume A is cooperative in the Gricean sense. 4 The proposition q that Sandy’s work was excellent is more

informative than p, the content of A’s utterance.

5 q is as polite and easy to express in this context as p. 6 By 1 , q is more relevant than p. 7 By 3 – 6 , A must lack sufficient evidence to assert q. 8 By 2 , A must lack evidence for q because q is false.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

(Scalar) Implicature calculation

Example

A: Sandy’s work this term was satisfactory. Implicature: Sandy’s work was not excellent (= ¬q)

1 Contextual premise: the speaker A intends to exhaustively

answer ‘What was the quality of Sandy’s work this term?’

2 Contextual premise: A has complete knowledge of Sandy’s

work for the term (say, A assigned all the grades for the class).

3 Assume A is cooperative in the Gricean sense. 4 The proposition q that Sandy’s work was excellent is more

informative than p, the content of A’s utterance.

5 q is as polite and easy to express in this context as p. 6 By 1 , q is more relevant than p. 7 By 3 – 6 , A must lack sufficient evidence to assert q. 8 By 2 , A must lack evidence for q because q is false.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Properties of conversational implicatures

1 Context dependence 2 Linguistic dependence 3 Cognitive complexity 4 Uncertainty (and re-enforceability) 5 Post-semanticality

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Cancelability

• Cancelability is not a consequence of Grice’s (1975) definition.
• The definition seems to leave room for cancelation in

particular cases, but it does not ensure it for all.

• Cancelation always compromises the speaker’s cooperativity

to some degree.

◮ In many cases, this is tolerable. ◮ If the compromises are too great, the speaker’s behavior

is uncooperative to the point of infelicity.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Scales and partial orders

Examples (Levinson 1983:134) all, most, many, some, few and, or n, . . . , 5, 4, 3, 2, 1 excellent, good hot, warm always, often, sometimes succeed, Ving, try to V, want to V necessarily p, p, possibly p certain that p, probable that p, possible that p must, should, may cold, cool love, like none, not all

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Scales and partial orders

Examples (A few other standard lexical scales) first, second, third, fourth, fifth definite, indefinite lover, friend need, want

• ld, middle-aged, young

general, colonel, major, captain, . . .

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Scales and partial orders

Examples (Mere partial orders; Hirschberg 1985:§5)

1

A: So, is she married? B: She’s engaged

106 So, S may affinn a nearer location Ii to convey lack of commitment r.o a farther one (i.e.,

• ,BEL(S, 9) or deny a fanher

to convey ....,BEL(S, ....,(li»· It also would seem that a speaker may declare ignorance of some location Ij to convey ....,BEL(S, -,(li» for Ii closer to S than and ....,BEL(S. It) for It further from S. So, B might convey that -,BEL(B. it gets to Thirty-sixth) and -,BEL(B. -,(it gets to Thirty-fourrh) by Ll,e response in 158. (158) A: Does this bus go up Walnut? B: I don't know if it gets to Thirty-fifth Street.

5.1.9. Process Stages and Prerequisites

Hamish implicitly recognizes the notion that process or prerequisite orderings may pennit scalar implicanare in his discussion of how the assenion

• X finished y may be vlewed as a

stronger remark than the assenion x started y. Since finishing 'entails' starring. 104 -- but not vice versa - the assertion of x started y implicates the falsity of x finished y, as when S implicates -,(159b) by saying (159a). (159)

• a. Minnie started mowing the lawn.
• b. Minnie finished mowlng the lawn.

This intuition seems correct, even though Hamish's explanation is unconvincing. lOS And the deniai offinish can be employed toimplicate ...,BEL(S. -.stan) -- thatfinish is the earliest stage some process S can truthfully deny. As far as S knows, earlier stages like starting are true.

1041n the sense that having [utisJu!d 'entails' having ,;t::vted. This is one example of the dispariry between Harnish's abstract characterization of entailment and the intllitive - and, here. teoporaHy-dependcnl - notion he is trying to capture. IOSA-::cording to Harnish., since {vtishing entails starting, x finishing 'j is to (viia). The denial cf (vlia) is (viib). (vii) (a) (lC started y) 1\ (."( fmished y) (b) ....,(x started y) v finished y) Cd) finished y) Clearly the trUth of the first disjunct of (viib) «vue» is su.:llCient (or :.he truth of !he disjunction. So S might deny (viia) simply by affirming (viie). By afftnning (what is., in effect) the disiun.:tion (viib) S thus makes a weaker statement than would be relevant and suitable if sihe couid tr'JLltfully affirr.t (viic). So it must be: L":at (viic) is false, i.e..... ":atx started 'j is true. Of course. !he truth of the second disjunct of (viib) «vlid) is also suft"icient for the

tr

..th of (vub). So, by same fl"..asoning we might conclude t.'lat S is unable to affirm (vlid) and t.'1at.x {mished J is [rue: The

• f

is that Hamish defines finish in tc:rtns of itself (:.c.• .t j!

..'lishirtg 'j is equivalent w (viiJ))

and assumes an implicit ordering of conjun..:ts Which hi:; notatioo does not support.

107 Note that, in exchanges such as 160. B provides an indirect response to A's query, which we might (160) A: Did you finish this? B: I didn't start it. interpret as an attempt to block the implicature that could be licensed by a simple denial of finish -- i.e., that tower values such as start are true or unknown to B. Orderings such as these may be seen as stages of a process or prerequisite orderings and suppon scalar implicature. For example, assume the following ordering:

• going ....----------- engagement ..

/'

steady

• -.......

dating marriage

'----

.

Then we can explain dle following impticatures as affirmations of stages in this process: In 161, B implicates that the woman in question is (161) A: So, is she mamed? B: She's engaged. not married by affirming that she is engaged. Note that this response will not commit B to the truth of going steady, for example, although this state may sometimes precede' engagement: So, process orderings need not be linear. But note that expressions which may be seen as denoting process stages need not acruaHy serve this function. In some contexts. for example, taking the GRE's, writing a thesis, doing a project, taking a comprehensive exam laking prerequisites and taking electives might be modeled as stages in a process of completing a Computer Science major. But it seems dear that, in an exchange like 162, these expressions are better seen (162) A: O.K. And for Barnard students, they had to take either GRE or write a thesis, right? But for Computer Science I don't know what to do. Is there 'my project or...? B: No, no, not. Our Depanment doesn't require any project neid1er a comprehensive exam. so all you need to do is fulfill the requirements which are a couple of prerequisites and four electives. as In unordered set of prerequisites, rather than as stages in a temporally ordered pro<::ess. \Vhen orderings like these do include alternative or optiOl':at paths. such branching nodes may be seen. like hierarchical siblings, as alternate values in the ordering. For example, signing a letter may be preceded optionally by proofreading it, and also by the alternate stages of typing the letter or writing it out by hand. as represented below:

2

A: Do you speak German? B: My husband does.

128 AFFIRt-.t(B,rhird chaprer, BEL(B, read(B,third chapter»»

1\

ALT_SENT(read(B,ch3prec_one), read(B,thirdj:hapter), 'parts of a dissertation'»

=) SCALAR_IMP(B, A, I read the third, ...,BEL(B, read(B,chapter_one»,

Ci) There are no restrictions on those posers which support scalar implicamre. However, (ar least) one restriction does exist on which posers may be viewed as salient in a given exchange: Above (Section 5.1.6.3) I noted for most metrics that rank utterances. both a given metric and irs dual (converse) may be candidares for salience in an exchange. However, no metric (ji which orders values vi and Vj such that a) vi is higher than Vj and b} the truth of Vj entails the truth of vi can supporr scalar implicature -- for the simple reason in such a case, a sentence Pi ranked higher than a sentence Pj by (ji since then the implicature licensed would be inconsistent with the utterance licensing it. In terms of the fonnaIism presented in Chapter 2, such a meaning would not be reinforceable. Consider, for example. (212a): (212) A: Are you planning to buy a dog?

• a. B: A German Shepherd.
• b. B: I'm buying a Gennan Shepherd and I'm not buying a dog.

While one might identify either an ordering defined by 'isa' (i.e.• a Gennan Shepherd isa dog)

• r by 'subsumes' (i.e., a dog subsumes the subtype

Shepherd) as salient in this exchange, only the latter permits scalar implicature here. B cannot implicate that she is not buying a dog vla this response, since buying a Gennan Shepherd entails buying a dog. The attempted reinforcement of (212b) fails. However, we cannot rule out 'isa' relations as potential supporters of scalar imphcarore: In 213, for example. 8's response might evoke either an'isa' (213) A: Would you like a dog? 8: I'd like a German Shepherd. hierarchy - or irs dual. Apparently, any poser can support scaiar implicature, although other tests for conversational implicature may rule out some particuJ3r posers in panicular exchanges.

5.3.2.3. Representing Scalar Implicature Orderings as Pos.ets J have demonstrated above how part! whole re!arions can be represented. To demonstrate

that L'le other orderings discussed in Section 5.1 are accounted for by a poset condition. I w;i! describe how representative orderings can be accommodated by this condition so mat scalar implicatures are correctly predicted by ImPl_3' Rdations defined by ordering the non-null members of the power

• f some set x by

set-inclusion allow a poset representation of x and its non-null proper subsets a5 follows: Any non-null proper subset of a set m<lY be nnked as LOWER than the set which

• it. and

129 set, in consequence, will represent a HIGHER value in the ordering. Subs.ets which are neither included in, nor include, one another, will be ALTERNATE values in this poser. Consider how the salient ordering in the following exchange mighr be represented: (214) A: Do you speak: Portuguese? B: My husband does. The inclusion ordering which supports the implicature in 214 might be represented as follows:

So, {husband,wife.chiid} wi!! be the highest value in this ordering, with the alternate doubletons (husband,wife), (wife.child), and (husband,child) lower values and the alternate values, {husband}, (wife), and {child} lower values still in this poset. By the scalar implicature conventions, then, S may affinn. say, (husband.wife) to convey ...,BEL(S. (husband,wile.child)) as well as -,BEL(S. (husband,child}) and -,BEL(S, (wife.childJ). Note, particularly, that there may be some redundance in scalar implicatures predicted from this representation. Also, any subsets so represented may be lexica1ized in various ways -- as, the expression (husband.wife) might be lexicalized as •couple' or as 'husband and wife'. The theory presented in this thesis will not distinguish between these. 128 As noted in Sections 5.1.7, temporal orderings may also be represented as setS ofrernporal for the analysis of licensed scalar implicacures. So, these orderings too wilt be defined by set inclusion, as: {past, resent} {presenr,future} {past,furure}

r---:::--

{future} Posers defined by a type! subrype metric, such as that which supports 174, may be illustrated by me (parrial) classification hierarchy:

lZ1!Sut see {CorelIa 84, Ka!ita 84) for some approaches to thtS problem.

3

A: Are you on your honeymoon? B: Well, I was.

128 AFFIRt-.t(B,rhird chaprer, BEL(B, read(B,third chapter»»

1\

ALT_SENT(read(B,ch3prec_one), read(B,thirdj:hapter), 'parts of a dissertation'»

=) SCALAR_IMP(B, A, I read the third, ...,BEL(B, read(B,chapter_one»,

Ci) There are no restrictions on those posers which support scalar implicamre. However, (ar least) one restriction does exist on which posers may be viewed as salient in a given exchange: Above (Section 5.1.6.3) I noted for most metrics that rank utterances. both a given metric and irs dual (converse) may be candidares for salience in an exchange. However, no metric (ji which orders values vi and Vj such that a) vi is higher than Vj and b} the truth of Vj entails the truth of vi can supporr scalar implicature -- for the simple reason in such a case, a sentence Pi ranked higher than a sentence Pj by (ji since then the implicature licensed would be inconsistent with the utterance licensing it. In terms of the fonnaIism presented in Chapter 2, such a meaning would not be reinforceable. Consider, for example. (212a): (212) A: Are you planning to buy a dog?

• a. B: A German Shepherd.
• b. B: I'm buying a Gennan Shepherd and I'm not buying a dog.

While one might identify either an ordering defined by 'isa' (i.e.• a Gennan Shepherd isa dog)

• r by 'subsumes' (i.e., a dog subsumes the subtype

Shepherd) as salient in this exchange, only the latter permits scalar implicature here. B cannot implicate that she is not buying a dog vla this response, since buying a Gennan Shepherd entails buying a dog. The attempted reinforcement of (212b) fails. However, we cannot rule out 'isa' relations as potential supporters of scalar imphcarore: In 213, for example. 8's response might evoke either an'isa' (213) A: Would you like a dog? 8: I'd like a German Shepherd. hierarchy - or irs dual. Apparently, any poser can support scaiar implicature, although other tests for conversational implicature may rule out some particuJ3r posers in panicular exchanges.

5.3.2.3. Representing Scalar Implicature Orderings as Pos.ets J have demonstrated above how part! whole re!arions can be represented. To demonstrate

that L'le other orderings discussed in Section 5.1 are accounted for by a poset condition. I w;i! describe how representative orderings can be accommodated by this condition so mat scalar implicatures are correctly predicted by ImPl_3' Rdations defined by ordering the non-null members of the power

• f some set x by

set-inclusion allow a poset representation of x and its non-null proper subsets a5 follows: Any non-null proper subset of a set m<lY be nnked as LOWER than the set which

• it. and

129 set, in consequence, will represent a HIGHER value in the ordering. Subs.ets which are neither included in, nor include, one another, will be ALTERNATE values in this poser. Consider how the salient ordering in the following exchange mighr be represented: (214) A: Do you speak: Portuguese? B: My husband does. The inclusion ordering which supports the implicature in 214 might be represented as follows:

So, {husband,wife.chiid} wi!! be the highest value in this ordering, with the alternate doubletons (husband,wife), (wife.child), and (husband,child) lower values and the alternate values, {husband}, (wife), and {child} lower values still in this poset. By the scalar implicature conventions, then, S may affinn. say, (husband.wife) to convey ...,BEL(S. (husband,wile.child)) as well as -,BEL(S. (husband,child}) and -,BEL(S, (wife.childJ). Note, particularly, that there may be some redundance in scalar implicatures predicted from this representation. Also, any subsets so represented may be lexica1ized in various ways -- as, the expression (husband.wife) might be lexicalized as •couple' or as 'husband and wife'. The theory presented in this thesis will not distinguish between these. 128 As noted in Sections 5.1.7, temporal orderings may also be represented as setS ofrernporal for the analysis of licensed scalar implicacures. So, these orderings too wilt be defined by set inclusion, as: {past, resent} {presenr,future} {past,furure}

r---:::--

{future} Posers defined by a type! subrype metric, such as that which supports 174, may be illustrated by me (parrial) classification hierarchy:

lZ1!Sut see {CorelIa 84, Ka!ita 84) for some approaches to thtS problem.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

A simple reference game

Example

r1 r2 ‘hat’ ‘glasses’ r1 F T r2 T T

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

A simple reference game

Example

r1 r2 ‘hat’ ‘glasses’ r1 F T r2 T T (A) Assume the speaker is cooperative. (B) ‘glasses’ is less informative that ‘hat’. (C) To reconcile ‘glasses’ with (A), assume the speaker lacks evidence for ‘hat’. (D) By the nature of the game, the speaker lacks evidence for ‘hat’ iff ‘hat’ is false.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Scalar implicatures: the theoretical landscape

Noncism

Russell 2006; Geurts 2011

Neo-Griceanism

Horn 1984; Sauerland 2001

Impliciture/Explicature

Bach 1994; Sperber & Wilson 1995

Presumptive/Generalized

Grice 1975; Levinson 2000

Logical Forms

Chierchia et al. 2012

Interactional Grammar-driven

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

1 Conversational implicature 2 Interactional models of implicature 3 Grammar-driven models of implicature 4 Embedded implicatures 5 Uncancelable implicatures

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Iterated Bayesian models

r1 r2

(a) Scenario

‘hat’ ‘glasses’ r1 F T r2 T T

(b) ·

r1 0.5 r2 0.5

(c) Prior

‘hat’ ‘glasses’

(d) Costs

Figure: A communication game supporting a scalar implicature.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Iterated Bayesian models

r1 r2

(a) Scenario

‘hat’ ‘glasses’ r1 F T r2 T T

(b) ·

r1 0.5 r2 0.5

(c) Prior

‘hat’ ‘glasses’

(d) Costs

Figure: A communication game supporting a scalar implicature. ‘hat’ ‘glasses’ r1 1 r2 0.5 0.5 S0 r1 r2 ‘hat’ 1 ‘glasses’ 0.67 0.33 L(S0) ‘hat’ ‘glasses’ r1 1 r2 0.75 0.25 S(L(S0)) Figure: The faces implicature in production and interpretation.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Iterated Bayesian models

r1 r2

(a) Scenario

‘hat’ ‘glasses’ r1 F T r2 T T

(b) ·

r1 0.5 r2 0.5

(c) Prior

‘hat’ ‘glasses’

(d) Costs

Figure: A communication game supporting a scalar implicature. ‘hat’ ‘glasses’ r1 1 r2 0.5 0.5 S0 r1 r2 ‘hat’ 1 ‘glasses’ 0.67 0.33 L(S0)

Iteration L(r1 | 'glasses')

1 5 10 15 20 0.00 0.67 1.00

Figure: The faces implicature in production and interpretation.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Priors and context dependence

r1 r2

(a) Scenario

‘hat’ ‘glasses’ r1 F T r2 T T

(b) ·

r1 0.5 r2 0.5

(c) Prior

‘hat’ ‘glasses’

(d) Costs

Figure: A communication game supporting a scalar implicature. r1 r2 ‘hat’ 1 ‘glasses’ 0.46 0.54

(a) L(S0) for P(r1) = 0.3.

P(r1) L(r1 | 'glasses')

0.00 0.34 1.00 0.0 0.5 1.0

Figure: The influence of the prior.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Priors in Frank & Goodman 2012

rbs rbc rgs blue green square circle rbs T F T F rbc T F F T rgs F T T F

C

to the

1 2 3

20 40 60

Prior: Salience Condition Bet

×

1 2 3

rbs rbc rgs blue 0.6 0.4 green 1 square 0.6 0.4 circle 1 Table: Listener, no priors rbs rbc rgs blue 0.4 0.6 green 1 square 0.4 0.6 circle 1 Table: Listener with priors.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Priors in Stiller et al. (2011)

2 3 4 adult Age Percent correct 20 40 60 80 100

Adqhoc*pragma3c*inference*

+ 3 other stimulus sets

N=24*per*group*

!

S3ller,*Goodman,*&*Frank*(2011)*

Slides from Mike Frank

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Priors in Stiller et al. (2011)

Distracting elements are really distracting:

“scales”

(0 0) (1 0) (1 1) (0 0) (1 1)

“no scales”

(1 0)

Slides from Mike Frank

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Priors in Stiller et al. (2011)

difying*speaker*produc3on*probabil

Here*are*all*the*people:* My friend*has*glasses.*** Can*you*show*me*my*friend?

Slides from Mike Frank

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Priors in Stiller et al. (2011)

ase*rate*data*

0* 25* 50* 75* 100*

10* 20* 30* 40* 50* 60* 70* 80* 90*

Proportion of faces without top hat in familiarization (%)

N=432*

Slides from Mike Frank

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Costs and linguistic dependence

r1 r2

(a) Scenario

‘hat’ ‘glasses’ r1 F T r2 T T

(b) ·

r1 0.5 r2 0.5

(c) Prior

‘hat’ ‘glasses’

(d) Costs

Figure: A communication game supporting a scalar implicature.

C('hat') S('glasses' | r2)

2 4 6 8 10 0.0 0.5 1.0

(a) S0

C('hat') L(r1 | 'glasses')

2 4 6 8 10 0.00 0.50 0.67 1.00

(b) L(S0)

C('hat') S('glasses' | r2)

2 4 6 8 10 0.00 0.25 0.50 1.00

(c) S(L(S0))

Figure: The influence of costs.

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Cognitive complexity and bounded rationality

r1 r2 r3 (a) Scenario. ‘hat’ ‘glasses’ ‘mustache’ r1 F F T r2 F T T r3 T T F (b) · ‘hat’ ‘glasses’ ‘mustache’ r1 1 r2 .5 .5 r3 .5 .5 (c) S0 r1 r2 r3 ‘hat’ 0 1 ‘glasses’ 0 .5 .5 ‘mustache’ .67 .33 0 (d) L(S0) ‘hat’ ‘glasses’ ‘mustache’ r1 1 r2 .6 .4 r3 .67 .33 (e) S(L(S0)) r1 r2 r3 ‘hat’ 1 ‘glasses’ 0 .64 .36 ‘mustache’ .71 .29 (f) L(S(L(S0)))

Figure: A complex faces scenario. The S(L(S0)) agent does not interpret ‘glasses’ pragmatically, but the L(S(L(S0))) agent does.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Extremely bounded rationality

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Uncertainty about . . .

• the context
• the linguistic norms
• the speaker’s preferred way to resolve tensions in the maxims
• the speaker’s commitment to cooperativity
• the speaker’s ability to undertake the necessary reasoning
• the listener’s beliefs about the speaker’s abilities
• . . .

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Post-semanticality

Generalizes the idea: each successively higher (more pragmatic) level is derived from a more literal lower level, beginning with (probabilistic) truth conditions: L(S(. . . (L(S0))))

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Related models

• Golland et al. (2010): L(S0)
• Frank & Goodman (2012): L(S(L(S0))), with only the outer

listener incorporating the prior.

• Vogel et al. (2013): L(S(L0)) and L(S0) embedded in a

multi-agent model of sequential decision making under uncertainty called the Decentralized Partially Observable Markov Decision Process.

• Franke (2008, 2009) and J¨

ager (2007, 2012): Best Response versions of the above.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Bayesian and Best Response models

‘hat’ ‘glasses’ r1 1 r2 0.5 0.5

(a) S0

r1 r2 ‘hat’ 1 ‘glasses’ 0.67 0.33

(b) L(S0)

P(r1) L(r1 | 'glasses')

0.00 0.34 1.00 0.0 0.5 1.0

Figure: Softmax model. ‘hat’ ‘glasses’ r1 1 r2 0.5 0.5

(a) S0

r1 r2 ‘hat’ 0 1 ‘glasses’ 1 0

(b) L br(S0)

P(r1) L(r1 | 'glasses')

0.00 0.34 1.00 0.0 0.5 1.0

Figure: Best-response model.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Relation to other phenomena

• Lewis’s (1969) signaling systems (H. Clark 1996).
• Implicatures encourage mutual exclusivity, a.k.a., the

pidgeon-hole principle (E. Clark 1987; Frank et al. 2009).

• Implicatures are modulated by the discourse participants’

questions, goals, and preferences (van Rooy, 2003; Benz, 2005; Vogel et al., 2013).

• Implicatures are a window into the interactions between

sentence-processing and high-level contextual understanding (Grodner & Sedivy, 2008; Huang & Snedeker, 2009; Grodner et al., 2010; Asher & Lascarides, 2013).

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

1 Conversational implicature 2 Interactional models of implicature 3 Grammar-driven models of implicature 4 Embedded implicatures 5 Uncancelable implicatures

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Grammar models

Chierchia et al. (2012):

“More specifically, the facts suggest that SIs are not pragmatic in nature but arise, instead, as a consequence of semantic or syntactic mechanisms, which we’ve characterized with the

• perator, O. This operator, although inspired by Gricean reasoning,

must be incorporated into the theory of syntax or semantics, so that — like the overt operator only — it will find its way to embedded positions.”

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Position in the theoretical landscape

Noncism

Russell 2006; Geurts 2011

Neo-Griceanism

Horn 1984; Sauerland 2001

Impliciture/Explicature

Bach 1994; Sperber & Wilson 1995

Presumptive/Generalized

Grice 1975; Levinson 2000

Logical Forms

Chierchia et al. 2012

Interactional Grammar-driven

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Exhaustification

Definition (Exhaustification operator)

OALT(p) = p ∧ ∀q ∈ ALT : (p ⊑ q) ⊑ ¬q

(Spector, 2007; Fox, 2007, 2009; Magri, 2009; Chierchia et al., 2012)

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Scalar implicatures in logical forms

Definition (Exhaustification operator)

OALT(p) = p ∧ ∀q ∈ ALT : (p ⊑ q) ⊑ ¬q

Example (Logical form)

OALT(p∨q)={p∧q}(p ∨ q) = {w2, w3} OALT(p∨q)={p∧q} p ∨ q = {w1, w2, w3} p = {w1, w2} ∨ q = {w1, w3}

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Scalar implicatures in logical forms

Definition (Exhaustification operator)

OALT(p) = p ∧ ∀q ∈ ALT : (p ⊑ q) ⊑ ¬q

Example (Logical form)

Kim VP believe OALT(p∨q)={p∧q}(p ∨ q) = {w2, w3} OALT(p∨q)={p∧q} p ∨ q = {w1, w2, w3} p = {w1, w2} ∨ q = {w1, w3}

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Scalar implicatures in logical forms

Definition (Exhaustification operator)

OALT(p) = p ∧ ∀q ∈ ALT : (p ⊑ q) ⊑ ¬q

Example (Logical form)

if OALT(p∨q)={p∧q}(p ∨ q) = {w2, w3} OALT(p∨q)={p∧q} p ∨ q = {w1, w2, w3} p = {w1, w2} ∨ q = {w1, w3}

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Implicit interactionality

Chierchia et al. (2012)

“the facts suggest that SIs are not pragmatic in nature but arise, instead, as a consequence of semantic or syntactic mechanisms”

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Implicit interactionality

Chierchia et al. (2012)

“the facts suggest that SIs are not pragmatic in nature but arise, instead, as a consequence of semantic or syntactic mechanisms”

Resolving underspecification pragmatically

The grammatical system specifies a many-to-one mapping from surface forms to logical forms. Only a pragmatic theory can explain how discourse participants coordinate on these LFs.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Implicit interactionality

Chierchia et al. (2012)

“the facts suggest that SIs are not pragmatic in nature but arise, instead, as a consequence of semantic or syntactic mechanisms”

Resolving underspecification pragmatically

The grammatical system specifies a many-to-one mapping from surface forms to logical forms. Only a pragmatic theory can explain how discourse participants coordinate on these LFs.

Chierchia et al. (2012)

“one can capture the correlation with various contextual considerations, under the standard assumption [. . . ] that such considerations enter into the choice between competing representations (those that contain the operator and those that do not).”

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Coordinating on a logical form in context

Essentially all the properties of implicature that I discussed earlier are predicted to hold on this theory as well.

Example

A: Sandy’s work this term was satisfactory. Potential implicature: Sandy’s work was not excellent Available logical forms: Sandy’s work was

1 satisfactory 2 OALT(satisfactory)={excellent}(satisfactory) 3 OALT(satisfactory)={good,excellent}(satisfactory)

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

1 Conversational implicature 2 Interactional models of implicature 3 Grammar-driven models of implicature 4 Embedded implicatures 5 Uncancelable implicatures

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Cases considered here and in the paper

Logical forms Gricean response Attitude embedding Facts follow from implicature reasoning Conditional antecedents Apparent embedding is an arti- fact of truth-functional analysis Hurford’s (1974) constraint

• n disjunction

Questioning the constraint Intrusive implicatures . . . Non-monotone quantifiers . . .

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Attitude embedding

Example

George believes that some of his advisors are crooks. Implicature: George believes not all of his advisors are crooks.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Attitude embedding

Example

George believes that some of his advisors are crooks. Implicature: George believes not all of his advisors are crooks.

Grammatical analysis

believes (advisor ∩ crook ∅) ∧ (advisor crook) {B | advisor ∩ B ∅} ∩ {B | advisor B} O[some→{all}](some) = some-and-not-all O[some→{all}] some

• f his advisors

are crooks

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Attitude embedding

Example

George believes that some of his advisors are crooks. Implicature: George believes not all of his advisors are crooks.

Gricean calculation (Russell, 2006)

1 Contextual assumption:

• G. believes all his advisors are crooks ∨
• G. believes not all his advisors are crooks

(p ∨ q)

2 Standard Gricean implicature:

not

• G. believes all of his advisors are crooks
• .

¬p

3 From 1 – 2 and disjunctive elimination:

• G. believes not all of his advisors are crooks.

q

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Conditional antecedents

Example

S If you take phonology or semantics, you attend meeting A. If you take both, you attend meeting B. Implicature: If you take phonology or semantics but not both . . .

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Conditional antecedents

Example

S If you take phonology or semantics, you attend meeting A. If you take both, you attend meeting B. Implicature: If you take phonology or semantics but not both . . .

A classical contradiction

If we interpret the disjunctive antecedent inclusively, contradiction:

1 (phono ∨ sem) → a 2 (phono ∧ sem) → b 3 (a ∧ b) → ⊥ 4 By 2 , 1 & transitivity: (phono ∧ sem) → a 5 By 2 and 4 : (phono ∧ sem) → (a ∧ b) 6 By 3 , 5 & transitivity: (phono ∧ sem) → ⊥

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Conditional antecedents

Example

S If you take phonology or semantics, you attend meeting A. If you take both, you attend meeting B. Implicature: If you take phonology or semantics but not both . . .

Grammatical analysis

If we exhaustify the disjunctive antecedent clause OALT(phono ∨ sem) → a then there is no contradiction: phono ¯

∨ sem and phono ∧ sem

are mutually exclusive, so there is no problem with having them lead to incompatible outcomes.

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Conditional antecedents

Example

S If you take phonology or semantics, you attend meeting A. If you take both, you attend meeting B. Implicature: If you take phonology or semantics but not both . . .

Kratzer–Lewis conditional (hat-tip to Dan Lassiter)

1 From the worlds that verify phono ∨ sem, select the subset

X of worlds that are most similar to the actual world.

2 a(w) = T for all w ∈ X.

w1 w2 w3 phono sem a b w1 T F T F w2 F T T F w3 T T F T

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Hurford’s constraint

Hurford’s (1974) constraint

“The joining of two sentences by or is unacceptable if one sentence entails the other; otherwise the use of or is acceptable.”

410 DISCUSSION

Now consider sentences (10) and (11). (10) Inmates may smoke or drink, but not both. (11) *Inmates may smoke or drink, and not both. If the general pattern of distribution of but not and and not characterized in (7) is followed here, then we must consider that inmates may both smoke and drink does not entail the negation of inmates may smoke or drink. That is, since (p and q) entails the negation of (p or q) just when the or is inter preted exclusively, we must consider the or in (10) and (11) to be inclusive. The semantic effect of the expression but not both here is to qualify or restrict an inclusive

• r in order to express exclusive disjunction.

Oddly, perhaps, an exactly parallel argument can be given to show that certain instances

• f or are exclusive.

Consider sentences (12)-(17). (12) Ivan is an American or a Russian. (13) That painting is of a man or a woman. (14) The value of x is greater than or equal to 6. (15) *John is an American or a Californian. (16) *That painting is of a man or a bachelor. (17) *The value of x is greater than or not equal to 6. Sentences (12)-(14) are acceptable; sentences (15)-(17) are not. The ap propriate generalization can be expressed as (18). (18) The joining of two sentences by or is unacceptable if

• ne sentence

entails the other; otherwise the use of or is acceptable.

Thus it follows from the fact that John is a Californian entails John is an

American that (15) is unacceptable. And (12) is acceptable because Ivan is a

Russian does not entail Ivan is an American and Ivan is an American does not entail Ivan is a Russian. The generalization in (18) is confirmed by the

unacceptability of sentence (19), in which a context is explicitly stipulated which determines entailment relations between sentences not logically related.

(19) *Jack and Jill travelled from Vienna to Paris together: he or she

went through Strasbourg. Now consider sentence (20), which is acceptable. (20) Inmates may smoke or, drink, or2 both.

(In this example, the two tokens of or are given subscript integers to dis

tinguish them in the exposition.) If the general pattern of or characterized

410 DISCUSSION

Now consider sentences (10) and (11). (10) Inmates may smoke or drink, but not both. (11) *Inmates may smoke or drink, and not both. If the general pattern of distribution of but not and and not characterized in (7) is followed here, then we must consider that inmates may both smoke and drink does not entail the negation of inmates may smoke or drink. That is, since (p and q) entails the negation of (p or q) just when the or is inter preted exclusively, we must consider the or in (10) and (11) to be inclusive. The semantic effect of the expression but not both here is to qualify or restrict an inclusive

• r in order to express exclusive disjunction.

Oddly, perhaps, an exactly parallel argument can be given to show that certain instances

• f or are exclusive.

Consider sentences (12)-(17). (12) Ivan is an American or a Russian. (13) That painting is of a man or a woman. (14) The value of x is greater than or equal to 6. (15) *John is an American or a Californian. (16) *That painting is of a man or a bachelor. (17) *The value of x is greater than or not equal to 6. Sentences (12)-(14) are acceptable; sentences (15)-(17) are not. The ap propriate generalization can be expressed as (18). (18) The joining of two sentences by or is unacceptable if

• ne sentence

entails the other; otherwise the use of or is acceptable.

Thus it follows from the fact that John is a Californian entails John is an

American that (15) is unacceptable. And (12) is acceptable because Ivan is a

Russian does not entail Ivan is an American and Ivan is an American does not entail Ivan is a Russian. The generalization in (18) is confirmed by the

unacceptability of sentence (19), in which a context is explicitly stipulated which determines entailment relations between sentences not logically related.

(19) *Jack and Jill travelled from Vienna to Paris together: he or she

went through Strasbourg. Now consider sentence (20), which is acceptable. (20) Inmates may smoke or, drink, or2 both.

(In this example, the two tokens of or are given subscript integers to dis

tinguish them in the exposition.) If the general pattern of or characterized

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Hurford’s constraint

Hurford’s (1974) constraint

“The joining of two sentences by or is unacceptable if one sentence entails the other; otherwise the use of or is acceptable.”

410 DISCUSSION

Now consider sentences (10) and (11). (10) Inmates may smoke or drink, but not both. (11) *Inmates may smoke or drink, and not both. If the general pattern of distribution of but not and and not characterized in (7) is followed here, then we must consider that inmates may both smoke and drink does not entail the negation of inmates may smoke or drink. That is, since (p and q) entails the negation of (p or q) just when the or is inter preted exclusively, we must consider the or in (10) and (11) to be inclusive. The semantic effect of the expression but not both here is to qualify or restrict an inclusive

• r in order to express exclusive disjunction.

Oddly, perhaps, an exactly parallel argument can be given to show that certain instances

• f or are exclusive.

Consider sentences (12)-(17). (12) Ivan is an American or a Russian. (13) That painting is of a man or a woman. (14) The value of x is greater than or equal to 6. (15) *John is an American or a Californian. (16) *That painting is of a man or a bachelor. (17) *The value of x is greater than or not equal to 6. Sentences (12)-(14) are acceptable; sentences (15)-(17) are not. The ap propriate generalization can be expressed as (18). (18) The joining of two sentences by or is unacceptable if

• ne sentence

entails the other; otherwise the use of or is acceptable.

Thus it follows from the fact that John is a Californian entails John is an

American that (15) is unacceptable. And (12) is acceptable because Ivan is a

Russian does not entail Ivan is an American and Ivan is an American does not entail Ivan is a Russian. The generalization in (18) is confirmed by the

unacceptability of sentence (19), in which a context is explicitly stipulated which determines entailment relations between sentences not logically related.

(19) *Jack and Jill travelled from Vienna to Paris together: he or she

went through Strasbourg. Now consider sentence (20), which is acceptable. (20) Inmates may smoke or, drink, or2 both.

(In this example, the two tokens of or are given subscript integers to dis

tinguish them in the exposition.) If the general pattern of or characterized

410 DISCUSSION

Now consider sentences (10) and (11). (10) Inmates may smoke or drink, but not both. (11) *Inmates may smoke or drink, and not both. If the general pattern of distribution of but not and and not characterized in (7) is followed here, then we must consider that inmates may both smoke and drink does not entail the negation of inmates may smoke or drink. That is, since (p and q) entails the negation of (p or q) just when the or is inter preted exclusively, we must consider the or in (10) and (11) to be inclusive. The semantic effect of the expression but not both here is to qualify or restrict an inclusive

• r in order to express exclusive disjunction.

Oddly, perhaps, an exactly parallel argument can be given to show that certain instances

• f or are exclusive.

Consider sentences (12)-(17). (12) Ivan is an American or a Russian. (13) That painting is of a man or a woman. (14) The value of x is greater than or equal to 6. (15) *John is an American or a Californian. (16) *That painting is of a man or a bachelor. (17) *The value of x is greater than or not equal to 6. Sentences (12)-(14) are acceptable; sentences (15)-(17) are not. The ap propriate generalization can be expressed as (18). (18) The joining of two sentences by or is unacceptable if

• ne sentence

entails the other; otherwise the use of or is acceptable.

Thus it follows from the fact that John is a Californian entails John is an

American that (15) is unacceptable. And (12) is acceptable because Ivan is a

Russian does not entail Ivan is an American and Ivan is an American does not entail Ivan is a Russian. The generalization in (18) is confirmed by the

unacceptability of sentence (19), in which a context is explicitly stipulated which determines entailment relations between sentences not logically related.

(19) *Jack and Jill travelled from Vienna to Paris together: he or she

went through Strasbourg. Now consider sentence (20), which is acceptable. (20) Inmates may smoke or, drink, or2 both.

(In this example, the two tokens of or are given subscript integers to dis

tinguish them in the exposition.) If the general pattern of or characterized

1 Violates HC: (smoke ∨ drink) ∨ (smoke ∧ drink) 2 Respects HC: OALT(smoke ∨ drink) ∨ (smoke ∧ drink)

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Hurford’s constraint — Is the constraint real?

We must first make exceptions for cases where the disjuncts are intended as synonyms:

Example

1 She’s an oenophile or wine lover

Apparent counterexamples found via Google N-grams and the WordNet hypernym hierarchy:

Examples (From the Web)

2 Stop discrimination of an applicant or person 3 Promptly report any accident or occurrence. 4 Recreational boat or vessel accidents are generally covered

by general maritime tort law.

5 Visits by Copts to the Holy Land can hardly be regarded as

treason or crime.

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Hurford’s constraint — Is the constraint real?

Examples (From the Web)

6 The anchor will lie on the bottom and the canoe or boat will be

held by the streams current.

7 How to be a Bikini or Swimwear Model 8 I believe that music can change or affect your emotions. 9 When their resignation is accepted they become an emeritus

archbishop or bishop.

10 Why are you recommending angioplasty or surgery for me? 11 So the next time your home project calls for a caulk or sealant,

choose the name you trust.

12 Many state arbitration statutes contemplate motions to correct

• r modify being made to the tribunal directly.

13 Being a captain or officer is a privilege, and with that privilege

comes great responsibility.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Hurford’s constraint

Counterexamples to Hurford’s constraint: http://goo.gl/VAGqnB

• 161 to date
• 86 where Left ⊏ Right
• 75 where Left ⊐ Right
• Finding more is easy but boring (lots of Web searches).

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Intrusive constructions

Examples

1 If Jack and Jill get married {to each other}, then their parents

will have to see each other again.

2 Because he earns $40K, he can’t afford a house in Palo Alto. 3 Having three children is less work than having four. 4 It is safer to drive home and drink beer than it is to drink beer

and drive home.

5 It is better to eat some of the cake than it is to eat all of it.

See Wilson (1975); Carston (1988); Levinson (2000); Recanati (2003); Horn (2006); King & Stanley (2006); Simons (2013)

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Intrusive constructions

Levinson (2000:200):

“on a purely semantic basis should be self-contradictory”.

Russell (2006)

Phrases like eat some/all the cake are generics, with the some version crucially excluding situations in which all the cake was eaten, because these are not generic eat-some-cake situations. Predicts markedness for non-generic comparisons.

Geurts (2009:§73):

“I believe that, in cases like these, we are forced to admit that scalar terms give rise to local upper-bounding interpretations, which cannot be accounted for in terms of implicature; they are local quasi-implicatures.”

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

A case of local enrichment

Chemla & Spector (2011), experiment 2

Global Local ր ց Literal Exactly one letter is connected with some of its circles.

1 Literal meaning: one letter is connected with some or all of its

circles, the other letters are connected with no circle.

2 Global reading: one letter is connected with some but not all of its

circles, the other letters are connected with no circle.

3 Local reading: one letter is connected with some but not all of its

circles, the other letters may be connected with either none or all of their circles.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

A case of local enrichment

Chemla & Spector (2011), experiment 2

Global Local ր ց Literal Exactly one letter is connected with some of its circles.

• Griceans depend on implicature→literal and so can’t simulate Local.
• If Griceans can derive an implicature, it will be the Global one,

which is false in the Local scenario.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

A case of local enrichment

Chemla & Spector (2011), experiment 2

Global Local ր ց Literal Exactly one letter is connected with some of its circles. Background and discussion: Chemla 2009; Geurts & Pouscoulous 2009; Clifton & Dube 2010; Ippolito 2010; Sauerland 2010

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

The theoretical import of embedded implicatures

• The Gricean can adopt the LFs of this theory to explain

embedded implicatures.

• Embedded implicatures are still shaped by pragmatic forces,

so the Gricean’s contributions remain vital.

• The questions are therefore much narrower: what is the

nature of these phenomena?

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

1 Conversational implicature 2 Interactional models of implicature 3 Grammar-driven models of implicature 4 Embedded implicatures 5 Uncancelable implicatures

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Grice’s view

Grice (1975)

“‘Since, to assume the presence of a conversational implicature, we have to assume that at least the Cooperative Principle is being

• bserved, and since it is possible to opt out of the observation of

this principle, it follows that a generalized conversational implicature can be canceled in a particular case.”

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A recipe for obligatory implicatures

There are forms ϕ and ψ such that, relative to the current context,

1 ϕ ⊑ ψ, and 2 ψ is strictly more costly than φ.

Examples (Spector 2007; Magri 2009)

1 (p ∨ q) vs. p 2 (#always) tall 3 (#Some) Italians come from a warm country.

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Obligatory implicatures in the interactional model

Example (Spector 2007)

Contextual premise: the atoms of the molecule are inseparable.

1 #Some atoms went right. 2 The atoms went right.

‘some’ ‘the’ p T T

(a) ·

‘some’ 1 ‘the’

(b) Costs

‘some’ ‘the’ p 0.27 0.73

(c) S0

p ‘some’ 0.5 ‘the’ 0.5

(d) L(S0)

Figure: Where two forms are synonymous and one is more marked, the more marked one is infelicitous. A speaker who used the marked form would need semantic motivation, impossible with synonyms.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Uncertainty, uncancelability, and uncooperativity

• Uncancelable implicatures are an artifact of idealization.
• There is always doubt surrounding the relevant lexical and

contextual assumptions about synonymy.

• In any case, uncancelability and uncooperativity are related,

as the Gricean predicts:

1 Opt out of quantity: “p or q, and I’m not telling which!” 2 Never motivated: “p or q, in fact, p!”

See Lauer 2013

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Conclusion: interacting with grammar

1 Even if implicatures can be embedded in logical forms, they

are still exquisitely sensitive to high-level plans, goals, and preferences of the discourse participants.

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Conclusion: interacting with grammar

1 Even if implicatures can be embedded in logical forms, they

are still exquisitely sensitive to high-level plans, goals, and preferences of the discourse participants.

2 Kyle to Ellen: “I have $8.”

• a. Context A: Movie tickets cost $10.
• b. Context B: Movie tickets cost $8.

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Conversational implicature Interactional models Grammar-driven models Embedded Uncancelable Conclusion

Conclusion: interacting with grammar

1 Even if implicatures can be embedded in logical forms, they

are still exquisitely sensitive to high-level plans, goals, and preferences of the discourse participants.

2 Kyle to Ellen: “I have $8.”

• a. Context A: Movie tickets cost $10.
• b. Context B: Movie tickets cost $8.

3 Chierchia et al. (2012): “one can capture the correlation with

various contextual considerations, under the standard assumption [. . . ] that such considerations enter into the choice between competing representations (those that contain the operator and those that do not).”

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Conclusion: interacting with grammar

4 Logical Form theories tell us where a speaker can put certain

covert semantic operators.

5 It’s up to a theory of (inter)action and social cognition to tell us

◮ what the speaker did ◮ why she did it ◮ how the hearer will understand her discourse move. 44 / 44

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Conclusion: interacting with grammar

Noncism

Russell 2006; Geurts 2011

Neo-Griceanism

Horn 1984; Sauerland 2001

Impliciture/Explicature

Bach 1994; Sperber & Wilson 1995

Presumptive/Generalized

Grice 1975; Levinson 2000

Logical Forms

Chierchia et al. 2012

Interactional Grammar-driven

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