1. Lecture 4: Sorites 2 Finish Kamp 1981 Explain other context-based approaches (Kyburg & Morreau: fitting words)

2. Context for sorites: Kamp 1981 Context C is a deductively closed set of formulas, e.g., a. {small(1), small(2), ¬small(3)} b. {small(1), small(2), ¬small(4)} c. {small(1), small(6), ¬small(4)} C is incoherent xy(small(x) εC & ¬small(y) εC & (x~y V y<<x)) Suppose JND=2. Then a is incoherent (2~3) b is coherent (not 2~4) c is incoherent (4 << 6)

3. 3-valued truth definition: (Here only truth) [small(a)] C =1 iff b(small(b) εC & (b~a or a<<b)) [p q] C =1 iff [p] C =0 or [q] C {p} =1 [p&q] C =1 iff [p] C =1 and [q] C =1 and C {p,q} is coherent Let C contain small(0) and ¬small(1000). Then For every i, we have [small(i) small(i+1)] C =1 because [small(i+1)] C {small(i)} =1 But let p 1 = small(0) small(1), p 2 = small(1) small(2), etc. Then [p 1 &…&p 1000 ] C 1, since C {p 1,…&p 1000 } is not coherent

4. Kamp 1981 universal quantification: like conjunction [ x (x)] C =1 iff [ (a)] C =1 for all constants a & { (a)} is coherent Example: [ i(small(i) small(i+1))]=1 iff [small(1) small(2)] C =1 & …. & the set of all the instantiations is coherent

5. Analysis of paradox (Kamp 81) Every instance of the crucial premiss Is true, but –Not their conjunction –Not the quantified version Is the paradox valid?

6. The argument (n=1000) small(0) small(0) small(1), so small(1) small(1) small(2), so small(2) … small(999) small(1000), so small(1000)

7. Contextualised version of validity: If premisses are true then conclusion is true in the context that collects all premisses. Argument is valid. Key: If [small(i)] C =1 and [small(i) small(i+1)] C =1 then [small(i+1)] C {small(i)} =1

8. Kamp 1981: consequences The argument is deemed valid Each instantiation of the crucial premiss is true, but their conjunction is not Likewise, quantified version is false Drawback: in many ways, this is as nonclassical as earlier accounts. (Compare Kamps critique of these)

9. Veltman 1987 Variant of Kamps approach, based on an idea of Nelson Goodman (further developed by Michael Dummett) Key idea: whether two objects can be distinguished depends on other objects in the context a ~ b ~ c | | a << c If c is absent then a and b are indistinguishable. But with c present, a difference between a and b can be inferred: c is called a help element.

10. Notations (x << y) A x >x & ¬ h>>y) (x ~ y) A ¬(x << y) A & ¬(y << x) A

11. Veltman 1987 (sketch) Proposed to judge the truth of a discourse relative to a context determined by the discourse A context is coherent iff it satisfies this version of EOI: small(i) C & (i~i+1) C small(i+1) C When premisses small(i) small(i+1) are judged on their own, they must be true in any coherent context because the context determined is {i,i+1}: There is no help element, so (i~i+1) C, so EOI fires.

12. Veltman 1987 (sketch) But when taken together, the premisses determine a larger context. For example, (99~100) C is judged in C = {0,1,2,…,100}. Plenty of help elements, so (99~100) C does not hold, and EOI does not fire, so coherence does not imply that small(100).

13. Veltman 1987 (sketch) In fact, it is not possible that all premisses of the form (i~i+1) C are true in the context jointly determined by them (Suppose small(k) & ¬small(k+1). Then C must contain h such that (k<<h) & ¬(k+1 << h). Consequently, (k<<k+1) C and therefore C is incoherent.)

14. Veltman 1987 (sketch) Similar reasoning makes the quantified version of the crucial premiss false in any coherent model Comments: –A flaw in an otherwise appealing approach? (Similar to Kamp 81) –The idea of using context may be taken further. For instance, contexts may be built up from left to right –More importantly, linguistic approaches to context show that where there is context dependence there is ambiguity (e.g., Graeme Hirst, Computational Linguistics 1996)

15. Van Deemter 1992,1996 In NPs like `a small elephant, the predicate small is known to be context dependent: context is provided by the noun elephant. Discourse Representation Theory (DRT) suggests: context can also be built up through discourse: x 1. dinosaur(x 1 ) x 2. whale(x 2 ) x 3. x 3 =Jumbo small(Jumbo) For concreteness, focus on one way in which small(x) might depend on context. Notation: small(x) A, S(x) A

16. Some definitions small(x) A def |{yεA: y >x}| K A [x] = def {yεA: y<<x} G A [x] = def {yεA: y>>x}.......... x.......... K A [x] G A [x] (Dots represent the elements of the context A)

17. We have contextualised both ~ and small This leads to many possible versions of the Crucial premiss. In particular Plain version: S(x) A & x~y S(y) A {x} Sophisticated version: S(x) A & x~y A S(y) A {x}

18. What other contextualisations can you think of? Are they really different? Recall: Plain version: S(x) A & x~y S(y) A {x} Sophisticated version: S(x) A & x~y A S(y) A {x}

19. Plain: S(x) A & x~y S(y) A {x} Sophisticated: S(x) A & x~y A S(y) A {x} S(x) may be relativised to A or to A {x} x~y may be relativised to nothing, to A, to A {x} or to A {x,y} S(y) may be relativised to A or to A {x}, or to A {y} or to A {x,y} Each of these options is equivalent to either Plain or Sophisticated. For instance, x~y A x~y A {x,y}. S(y) A S(y) A {x,y} because x~y (in both versions)

20. First claim Plain version is invalid: ¬ S(x) A & x~y S(y) A {x} This is easy to see: let context A={x,y,z} x << z... x ~ y ~ z K A [x]={}, G A [x]={z}, therefore S(x) A K A [y]={}, G A [y]={}, therefore ¬S(y) A {x}

21. How about the sophisticated version? Is it valid? S(x) A & x~y A S(y) A {x}

22. Second claim Sophisticated version is valid: S(x) A & x~y A S(y) A {x} Proof: suppose S(a) A & a~b A. Now suppose ¬S(b) A {a} This is equivalent to ¬S(b) A. We therefore have S(a) A and ¬S(b) A, so either xεA(x >a & ¬x>>b). But then x is a help element for distinguishing a and b, contradicting a~b A

23. So, the premiss is ambiguous between a plain version that is invalid, and a sophisticated version that is valid Since there exists a valid version of the premiss, does this mean that the paradox follows? No, for the sophisticated version is not strong enough to support the (paradoxical) conclusion

24. Two further claims 1. Plain version supports sorites. 2. Sophisticated version does not support sorites. 1.Plain version supports sorites. Suppose S(x) A & x~y S(y) A {x} then sorites goes S(0) A so S(1) A {0} so S(2) A {0,1} so S(3) A {0,1,2}, etc.

25. 2. Sophisticated version does not support sorites. Suppose we use S(x) A & x~y A S(y) A {x} then the sorites chain breaks off: S(0) A, so [since (0~1) A ] S(1) A {0}, so [since (1~2) A {0} ] S(2) A {0,1}, so [since (2~3) A {0,1} ] S(3) A {0,1,2}, so [since (3~4) A {0,1,2} ], etc. Context for judging (i ~ i+1) grows, creating more and more help elements. Beyond some point, no suitable y can be found.

26. Proof Suppose i: (o~i) A {0,1,2,…i-1} Then certainly i: (o~i). If that is the case, all objects in the domain are indistinguishable from each other.

27. Illustration Suppose the argument concerns the heights of people. (S = short) Suppose A={ I }={speaker} Let size(x i+1 )-size(x i )=1mm Let JND=10mm Three types of situations can arise:

28. 1.We reach x 10, which is the first element to be indistinguishable from x 0 x 0 ~ x 9..... x 0 x 1 x 9 x 10 I..... x 0 << x 10 << I = x 400

29. 2. We reach x 10, which is the first element to be indistinguishable from the speaker x 9 << I.... x 1 x 9 x 10 I.... x 1 ~ x 10 ~ I = x 19

30. 3. Upon reaching x 10, both changes coincide x 0 ~ x 9 << I...... x 0 x 1 x 9 x 10 I..... x 0 << x 10 ~ I = x 19

31. In all three situations, two things happen at the same time: –While x 9 was indistinguishable from its predecessor, x 10 was not –While x 9 was short with respect to its context, x 10 was not

32. DRT-ish perspective on sorites: I x 0 S(x 0 ) {I} x 1 S(x 1 ) {I} {x 0 } x 2 S(x 2 ) {I} {x 0,x 1 } …

33. Properties of this solution In terms of Hydes classification: this approach depicts sorites as a fallacious argument. (Fallacy of Ambiguity) The pattern is as follows 1.Some readings of sorites have premisses all of which are true 2.Some readings of sorites are valid 3.There are no readings for which both (a) and (b) are true See paper in syllabus (section 4)

34. Properties of this solution Proposal is logically conservative 2-valued standard semantics for connectives Some empirical support: By working with formulas like Short(x) A, the context- dependence of `Short` has been taken into account, in the spirit of context-change semantics

35. Approach to meaning of gradable adjectives One could argue that the vagueness of gradable adjectives was denied –Fuzzy logic really models vagueness –n-valued logic models it to some approximation –Kamp 1981 and Veltman 1987 are similar to our proposal in this respect Maybe vagueness cannot be modelled classically?

36. Approach to vagueness EOI is done justice: Suppose the objects a and b are observationally indistinguishable in the respects relevant to P; then either a and b both satisfy P or else neither of them does. but observationally indistinguishable has been contextualised, using Goodman/Dummetts trick

37. Some nagging doubts Some recent proposals bank on Goodman/Dummet notion of a help element Read Dummett 1975: `Wangs Paradox`. Synthese 30 Strange predictions. Imagine a clock whose hands can be manually adjusted. Following Goodman any difference between two hands can be made `distinguishable by positioning the third hand. If experiments were done, one would find that indistinguishability was not always judged in the same way. (JNDs were defined with this in mind!) How do these considerations affect our proposals?

38. What can we learn from these proposals? A computational viewpoint: what should an NLP system understand about vagueness? What pitfalls should be avoided and how? Where the system employs vague relations like `similar, `equivalent, it should be aware of their non-transitivity. (unlike real equivalence, which is transitive.) The system should be able to reason with vagueness, at least in the style of Goyal & Shoham. It should not implement EOI, since this leads to paradox: fixed standards would be preferable!

39. When interpreting vague statements, the system should understand that speakers may be using different standards, and that these standards are dependent on context, which tends to lead to ambiguities When producing vague statements, it should be aware of the same unclarities, and preferably mirror the way people (e.g., the systems user) speak. Sometimes it may be better to use numbers. In this case, the system has to understand the notion of an approximation. This implies understanding –that a measured difference can be arbitrarily small –that no difference may be measured even though one exists –that `nice numbers have a vague reading More about these issues in the next lecture!

40. Semantics of vague adjectives Speakers can vary their standards `by fiat (Kennedy 1999, van Deemter 2000) m1:2cm, m2: 5cm, m3: 7cm, m4: 10cm –`The large mouse = m4 –`The two large mice = {m3,m4} Kyburg and Morreau (2000): `Just as a home handyman can fit an adjustable wrench to a nut, we think, a speaker can adjust the extension of a vague expression to suit his needs

41. How does this wrench work? A stab –at formalisation by Kyburg and Morreau –at implementation by DeVault & Stone (NLU) and Van Deemter (NLG) –Here: Kyburg and Morreau Their example: pigs in a pen. Language: –fat-pig(x) –at-least-as-fat-as(x,y) –fatter(x,y) –individual constants: Arnold, Babe –D(p) (p is Definitely the case)

42. One way of thinking about D: D records the things that the wrench cannot change D is similar to using three truth values: if Arnold is borderline-fat then ¬D(fat-pig(Arnold) ¬D¬(fat-pig(Arnold) Abbreviation: I(fat-pig(Arnold) (Arnolds being fat is Indeterminate)

43. Sketch of model theory Model M = Dom = Domain P = set of evaluation points = tree ordering of P @ = `appropriate point = bottom of tree L = interpretation function L + (R,p)=objects having property R at p L - (R,p)=objects having property R at p

44. Constraint on interpretation L: If pq then L + (R,p) L + (R,q) L - (R,p) L - (R,q) (moving up the tree, more predications get resolved) A point p is complete iff, for all R, L + (R,p) L - (R,p) = Dom

45. Truth at point p is roughly supervaluational : [R(a)] M,p = true/false iff for all complete q such that pq, [a] ε L + (R,q) [D( )] M,p = true iff [ ] M,@ (NB This makes [D( )] independent of p) is true/false/undefined in the model M iff is true/false/undefined at @ Consequence: (at complete and incomplete points) it may be that [R(a)] M,p = true while [D(R(a))] M,p = false

46. Example 1 Common ground: I(fat-pig(Arnold)) D(skinny-pig(Babe)) x(pig(x) x =Arnold V x=Babe) fatter(Arnold,Babe) Utterance: s = `the fat pig won the prize Entailed: fat-pig(Arnold),¬fat-pig(Babe) so, `the fat pig` = Arnold

47. Reasoning: s implies !x(fat-pig(x) Common Ground says D(skinny-pig(Babe)), so ¬fat_pig(Babe) Since Arnold is the only other pig, it follows that fat-pig(Arnold)

48. Example 2 Common ground: I(fat-pig(Arnold)) I(fat-pig(Babe)) x(pig(x) x =Arnold V x=Babe) fatter(Arnold,Babe) Utterance: s = `the fat pig won the prize Entailed: fat-pig(Arnold),¬fat-pig(Babe) so, the fat pig = Arnold

49. Example 3: revising the common ground fat-pig(Arnold) fat-pig(Babe) x(pig(x) x = Arnold V x = Babe) fatter(Arnold,Babe) s = `the pig that is not fat won the prize Entailed: fat-pig(Arnold),¬fat-pig(Babe) so, the pig that is not fat = Babe

50. Kyburg & Morreau (summing up) Intuitions about accommodation of word meaning seem spot on. Some aspects of `dynamics are captured Formal apparatus: some questions, e.g., how does the tree arise? E.g., does it incorporate laws saying large-pig(x) & larger(y,x) large-pig(y) ? Maybe the wrench is too adaptable. –After calling {Arnold,Babe} `the large pigs, can we call {Arnold} `the large pig ? –Can statements like fatter(Arnold,Babe) also be revised? Might this be embedded into a general theory of information update?

51. Ideas inspired by Kennedy / Kyburg & Morreau have been implemented in NLU by DeVault & Stone (syllabus) –They built a drawing application, where NLU system interprets instructions in context –`(fill) the large circle = (fill) the largest of the visible circles –Result (e.g.) [large(a)]=1; all others are small –until a new description forces a revision (e.g., `delete this circle` (?)) Applications to NLG (to follow)