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MGs SMNF Locality & Grammar Size Conclusion Grammar Size and Quantitative Restrictions on Movement Thomas Graf Stony Brook University [email protected] http://thomasgraf.net SCiL 2018 Jan 4–7, 2018

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Page 1: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Grammar Size and Quantitative Restrictionson Movement

Thomas Graf

Stony Brook [email protected]

http://thomasgraf.net

SCiL 2018Jan 4–7, 2018

Page 2: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Outline

1 Movement in Minimalist GrammarsMerge and MoveIntermediate Movement

2 Single Movement Normal Form

3 Movement Constraints and Grammar Size: A Curious Conspiracy

Page 3: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Minimalist Grammars (MGs)

É Minimalist grammars (MGs) are aformalization of Chomskyan syntax(Stabler 1997, 2011)

É Succinct formalism for defining MCFGs

É Operations: Merge and Move

É Grammar is just a finite list offeature-annotated lexical items (LIs)

Chemistry Syntaxatoms words

electrons featuresmolecules sentences

1

Page 4: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 5: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 6: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

DP

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 7: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

DP

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 8: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

DP DP

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 9: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

DP DP

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 10: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

DP DPV′

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 11: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

DP DPV′

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 12: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MergeMerge combines subtrees to encode head-argument dependencies.

category feature N− , V− , . . .

selector feature N+ , V+ , . . .

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

DP DPV′

VP

É the and men have matching features, triggering Merge

É same steps for which men

É like merged with which men

É like merged with the men

2

Page 13: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Merge in Derivation Trees

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

DP DPV′

VP

Derived Tree

the

N+ D−men

N−like

D+ D+ V−which

N+ D−men

N−

• ••

Derivation Tree

3

Page 14: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MoveMove displaces subtrees to derive the correct linear order.

licensee feature wh− , top− , . . .

licensor feature wh+ , top+ , . . .

theN+ D−

menN−

likeD+ D+ V−

whichN+ D− wh−

menN−

doV+ wh+ C−

DP DPV′

VP

É Merge do

É Move triggered by features of opposite polarity

4

Page 15: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MoveMove displaces subtrees to derive the correct linear order.

licensee feature wh− , top− , . . .

licensor feature wh+ , top+ , . . .

theN+ D−

menN−

likeD+ D+ V−

whichN+ D− wh−

menN−

doV+ wh+ C−

DP DPV′

VP

É Merge do

É Move triggered by features of opposite polarity

4

Page 16: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MoveMove displaces subtrees to derive the correct linear order.

licensee feature wh− , top− , . . .

licensor feature wh+ , top+ , . . .

theN+ D−

menN−

likeD+ D+ V−

whichN+ D− wh−

menN−

doV+ wh+ C−

DP DPV′

VPC′

É Merge do

É Move triggered by features of opposite polarity

4

Page 17: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MoveMove displaces subtrees to derive the correct linear order.

licensee feature wh− , top− , . . .

licensor feature wh+ , top+ , . . .

theN+ D−

menN−

likeD+ D+ V−

whichN+ D− wh−

menN−

doV+ wh+ C−

DP DPV′

VPC′

É Merge do

É Move triggered by features of opposite polarity

4

Page 18: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

MoveMove displaces subtrees to derive the correct linear order.

licensee feature wh− , top− , . . .

licensor feature wh+ , top+ , . . .

theN+ D−

menN−

likeD+ D+ V−

whichN+ D− wh−

menN−

doV+ wh+ C−

DP t

DP

V′VP

C′

CP

É Merge do

É Move triggered by features of opposite polarity

4

Page 19: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Move in Derivation Trees

theN+ D−

menN−

likeD+ D+ V−

whichN+ D− wh−

menN−do

V+ wh+ C−DP t

DP

V′VP

C′

CP

the

N+ D−men

N−like

D+ D+ V−which

N+ D− wh−men

N−

do

V+ wh+ C−•

••

••

5

Page 20: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Move in Derivation Trees

theN+ D−

menN−

likeD+ D+ V−

whichN+ D− wh−

menN−do

V+ wh+ C−DP t

DP

V′VP

C′

CP

the

N+ D−men

N−like

D+ D+ V−which

N+ D− wh−men

N−

do

V+ wh+ C−•

••

••

5

Page 21: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Intermediate Movement

Intermediate movement is possible, but has no effect on output.

do ◦

the men

v •

like •

which menN−N+ D− case− wh−

D+ D+ V−

N+ D− N− V+ case+ v−

v+ wh+ C−CP

DP

which men

C′

do vP

t vP

DP

the men

v′

v VP

like t

6

Page 22: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

An Issue with Intermediate Movement

Minimalist analyses posit an unboundednumber of intermediate landing sites.

(1) Who does John thinkt that Mary believest that Sue saidt that . . .t that Bill hates t?

But every LI can only carryfinitely many features!

does ◦

that ◦

that ◦

that •

hates who

7

Page 23: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Derivational Solution (Kobele 2006)

É Only final landing site has feature.

É Intermediate movement is inserted bymapping to phrase structure trees

Recipe for Successive Cyclic Movement

Add trace in every crossed Spec,CP.

does •

that •

that •

that •

hates who

8

Page 24: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Generalization: No Intermediate Movement (Graf et al. 2016)

Alëna Aksënova

Aniello De Santo

É Kobele’s solution can be generalized.

É Intermediate movement never needs to befeature triggered.

É It is derivationally redundant.

Definition (Single Movement Normal Form)

An MG is in single movement normal form (SMNF)iff every LI has at most one licensee feature.

Theorem

For every MG there is a strongly equivalent MGthat is in SMNF.

9

Page 25: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

An Annoying Complication

MGs have one central locality restriction on Move.

Shortest Move Constraint (SMC)

If two LIs in a tree both have a licensee feature as theirfirst currently unchecked feature, then these features must be distinct.

which

N+ D− wh−men

N−like

D+ D+ V−which

N+ D− wh−men

N−

DPV′

DP

VP

10

Page 26: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

An Annoying Complication

MGs have one central locality restriction on Move.

Shortest Move Constraint (SMC)

If two LIs in a tree both have a licensee feature as theirfirst currently unchecked feature, then these features must be distinct.

which

N+ D− wh−men

N−like

D+ D+ V−which

N+ D− wh−men

N−

DPV′

DP

VP

10

Page 27: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

An Annoying Complication

MGs have one central locality restriction on Move.

Shortest Move Constraint (SMC)

If two LIs in a tree both have a licensee feature as theirfirst currently unchecked feature, then these features must be distinct.

which

N+ D− wh−men

N−like

D+ D+ V−which

N+ D− wh−men

N−

DPV′

DP

VP

10

Page 28: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

An Annoying Complication

MGs have one central locality restriction on Move.

Shortest Move Constraint (SMC)

If two LIs in a tree both have a licensee feature as theirfirst currently unchecked feature, then these features must be distinct.

which

N+ D− wh−men

N−like

D+ D+ V−which

N+ D− wh−men

N−

DPV′

DP

VP

10

Page 29: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

An Annoying Complication

MGs have one central locality restriction on Move.

Shortest Move Constraint (SMC)

If two LIs in a tree both have a licensee feature as theirfirst currently unchecked feature, then these features must be distinct.

which

N+ D− wh−men

N−like

D+ D+ V−which

N+ D− wh−men

N−

DPV′

DP

VP

“Don’t cross the streams!”

10

Page 30: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

A Work-Around with SubscriptsFeature Subscripting

É For every LI l, only keep its last licensee feature.

É Add subscripts to licensee features to avoid SMC violations.

◦◦◦

b :: B− f− g− ◦

a :: A− f− g− x :: A+ X−y :: X+ f+ B+ Y−

c :: Y+ g+ f+ g+ C−

◦◦

b :: B− g1− •

a :: A− g0− x :: A+ X−

y :: X+ B+ Y−

c :: Y+ g0+ g1

+ C−

11

Page 31: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

A Work-Around with SubscriptsFeature Subscripting

É For every LI l, only keep its last licensee feature.

É Add subscripts to licensee features to avoid SMC violations.

◦◦◦

b :: B− f− g− ◦

a :: A− f− g− x :: A+ X−y :: X+ f+ B+ Y−

c :: Y+ g+ f+ g+ C−

◦◦

b :: B− g1− •

a :: A− g0− x :: A+ X−

y :: X+ B+ Y−

c :: Y+ g0+ g1

+ C−

11

Page 32: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Lexical Blow-Up

É SMNF translation induces linear lexical blow-up

É Effect varies a lot depending on movement configurations:

lower bound linear size reduction(!),1:1 for non-redundant grammars

upper bound large linear blow-up

l∈Lex

µγ(l)+δ(l)

µ . . . maximum number of required indicesγ(l) . . . number of licensor features of LI l in original grammarδ(l) . . . 1 if l has licensee features, 0 otherwise

12

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MGs SMNF Locality & Grammar Size Conclusion

Abstract Example (Sketch)

c :: C+ C− c :: C− g− f−◦

c :: C+ g+ f+ C+ g+ f+ C− g− f− ◦

c :: C− g− f− ◦

c :: C+ g+ C+ f+ C− c :: C− g− f−

13

Page 34: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Abstract Example (Sketch)

c :: C+ C− c :: C− g− f−◦

c :: C+ g+ f+ C+ g+ f+ C− g− f− ◦

c :: C− g− f− ◦

c :: C+ g+ C+ f+ C− c :: C− g− f−

13

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MGs SMNF Locality & Grammar Size Conclusion

Abstract Example (Sketch)

c :: C+ C− c :: C− g− f−◦

c :: C+ g+ f+ C+ g+ f+ C− g− f− ◦

c :: C− g− f− ◦

c :: C+ g+ C+ f+ C− c :: C− g− f−

13

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MGs SMNF Locality & Grammar Size Conclusion

Abstract Example (Sketch)

c :: C+ C− c :: C− g− f−◦

c :: C+ g+ f+ C+ g+ f+ C− g− f− ◦

c :: C− g− f− ◦

c :: C+ g+ C+ f+ C− c :: C− g− f−

13

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MGs SMNF Locality & Grammar Size Conclusion

Abstract Example (Sketch)

c :: C+ C− c :: C− g− f−◦

c :: C+ g+ f+ C+ g+ f+ C− g− f− ◦

c :: C− g− f− ◦

c :: C+ g+ C+ f+ C− c :: C− g− f−

13

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MGs SMNF Locality & Grammar Size Conclusion

Abstract Example (Sketch)

c :: C+ C− c :: C− g− f−◦

c :: C+ g+ f+ C+ g+ f+ C− g− f− ◦

c :: C− g− f− ◦

c :: C+ g+ C+ f+ C− c :: C− g− f−

13

Page 39: Grammar Size and Quantitative Restrictions on Movement · Generalization: No Intermediate Movement (Graf et al. 2016) Alëna Aksënova Aniello De Santo É Kobele’s solution can

MGs SMNF Locality & Grammar Size Conclusion

Abstract Example (Sketch)

c :: C+ C− c :: C− g− f−◦

c :: C+ g+ f+ C+ g+ f+ C− g− f− ◦

c :: C− g− f− ◦

c :: C+ g+ C+ f+ C− c :: C− g− f−

13

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MGs SMNF Locality & Grammar Size Conclusion

Abstract Example (the Math)

É The original grammar contains a single LI.

c :: C+ g+ f+ C+ g+ f+ C− g− f−

É The SMNF grammar contains 8 variants.

c :: C+ f+0 C+ f+0 C− f−0 c :: C+ f+0 C+ f+0 C− f−1c :: C+ f+0 C+ f+1 C− f−0 c :: C+ f+0 C+ f+1 C− f−1c :: C+ f+1 C+ f+0 C− f−0 c :: C+ f+1 C+ f+0 C− f−1c :: C+ f+1 C+ f+1 C− f−0 c :: C+ f+1 C+ f+1 C− f−1

É We can get n variants of the LI by changing the phonetic exponent,so the grammar size increases at least by 8n.

É But we can keep increasing number m of arguments:

2m+1 × n

14

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MGs SMNF Locality & Grammar Size Conclusion

Interim Summary

É Every MG is a finite set of LIs.

É The more LIs, the larger the grammar.

É Derivation trees are the primary data structure.É Intermediate movement is derivationally redundant and costly:

É complicates proofsÉ increases computational complexity (Graf and Heinz 2015)É at odds with MG processing models (Graf et al. 2017)

É But SMNF MGs may be much larger, which is bad forÉ parsingÉ learningÉ explanatory adequacy

A New Empirical Puzzle

Are the movement configurations we find in natural languageexactly those that induce little lexical blow-up?

15

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MGs SMNF Locality & Grammar Size Conclusion

Interim Summary

É Every MG is a finite set of LIs.

É The more LIs, the larger the grammar.

É Derivation trees are the primary data structure.É Intermediate movement is derivationally redundant and costly:

É complicates proofsÉ increases computational complexity (Graf and Heinz 2015)É at odds with MG processing models (Graf et al. 2017)

É But SMNF MGs may be much larger, which is bad forÉ parsingÉ learningÉ explanatory adequacy

A New Empirical Puzzle

Are the movement configurations we find in natural languageexactly those that induce little lexical blow-up?

15

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MGs SMNF Locality & Grammar Size Conclusion

What Produces a Large Blow-Up in Grammar Size?

É Large blow-up occurs whenever there are multiple LIs s.t.1 they all have the same final movement feature, and2 they have overlapping movement paths, and3 their relative configuration is not fixed across derivations.

É That’s easy to do with abstract examples,but natural examples are tough.

The Constraint-Grammar-Size Conspiracy

Patterns that would induce a large blow-up are independently forbidden.

16

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MGs SMNF Locality & Grammar Size Conclusion

What Produces a Large Blow-Up in Grammar Size?

É Large blow-up occurs whenever there are multiple LIs s.t.1 they all have the same final movement feature, and2 they have overlapping movement paths, and3 their relative configuration is not fixed across derivations.

É That’s easy to do with abstract examples,but natural examples are tough.

The Constraint-Grammar-Size Conspiracy

Patterns that would induce a large blow-up are independently forbidden.

16

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MGs SMNF Locality & Grammar Size Conclusion

Improper Movement

Merge

C Move

Merge

T Merge

John Merge

wonders Move

Merge

C Move

Merge

T Merge

Bill Merge

saw who

Merge

C Move

Merge

T Merge

wonders Move

Merge

C Move

Merge

T Merge

Bill Merge

saw who

nom+

wh+

nom+

nom−

nom−

wh−

nom+

wh+

nom+

nom−

wh− nom−

wh

nom

nom

wh

nom

nom

(2) John wonderswho Bill saw.

(3)

*

Who wonders Billsaw?

17

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MGs SMNF Locality & Grammar Size Conclusion

Improper Movement

Merge

C Move

Merge

T Merge

John Merge

wonders Move

Merge

C Move

Merge

T Merge

Bill Merge

saw who

Merge

C Move

Merge

T Merge

wonders Move

Merge

C Move

Merge

T Merge

Bill Merge

saw who

nom+

wh+

nom+

nom−

nom−

wh−

nom+

wh+

nom+

nom−

wh− nom−

wh

nom

nom

wh

nom

nom (2) John wonderswho Bill saw.

(3)

*

Who wonders Billsaw?

17

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MGs SMNF Locality & Grammar Size Conclusion

Improper Movement

Merge

C Move

Merge

T Merge

John Merge

wonders Move

Merge

C Move

Merge

T Merge

Bill Merge

saw who

Merge

C Move

Merge

T Merge

wonders Move

Merge

C Move

Merge

T Merge

Bill Merge

saw who

nom+

wh+

nom+

nom−

nom−

wh−

nom+

wh+

nom+

nom−

wh− nom−

wh

nom

nom

wh

nom

nom (2) John wonderswho Bill saw.

(3)

*

Who wonders Billsaw?

17

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MGs SMNF Locality & Grammar Size Conclusion

Improper Movement

Merge

C Move

Merge

T Merge

John Merge

wonders Move

Merge

C Move

Merge

T Merge

Bill Merge

saw who

Merge

C Move

Merge

T Merge

wonders Move

Merge

C Move

Merge

T Merge

Bill Merge

saw who

nom+

wh+

nom+

nom−

nom−

wh−

nom+

wh+

nom+

nom−

wh− nom−

wh

nom

nom

wh

nom

nom (2) John wonderswho Bill saw.

(3) * Who wonders Billsaw?

17

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MGs SMNF Locality & Grammar Size Conclusion

SuperraisingMerge

C Move

Merge

T Merge

seems Move

Merge

C Move

Merge

T Merge

Bill Merge

believes Merge

C Move

Merge

T Merge

John Merge

likes Mary

nom+

f+

nom+

nom+

nom−

nom− f− nom−

nom

f

nom

nom

(4)

*

John seems Bill believeslikes Mary

18

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MGs SMNF Locality & Grammar Size Conclusion

SuperraisingMerge

C Move

Merge

T Merge

seems Move

Merge

C Move

Merge

T Merge

Bill Merge

believes Merge

C Move

Merge

T Merge

John Merge

likes Mary

nom+

f+

nom+

nom+

nom−

nom− f− nom−

nom

f

nom

nom

(4)

*

John seems Bill believeslikes Mary

18

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MGs SMNF Locality & Grammar Size Conclusion

SuperraisingMerge

C Move

Merge

T Merge

seems Move

Merge

C Move

Merge

T Merge

Bill Merge

believes Merge

C Move

Merge

T Merge

John Merge

likes Mary

nom+

f+

nom+

nom+

nom−

nom− f− nom−

nom

f

nom

nom (4) * John seems Bill believeslikes Mary

18

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MGs SMNF Locality & Grammar Size Conclusion

Freezing Effects

É One could also get overlapping paths by extracting an f -moverfrom within an f -mover.

É But this produces freezing effects.

(5) It seems your comment about John annoys Sue.

(6) * John seems your comment about t annoys Sue.

(7) * Who don’t you know [which pictures of t] Mary bought.

19

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MGs SMNF Locality & Grammar Size Conclusion

Wh-Islands

É Multiple A′-movers of the same type would also be an option.

É But here the wh-island constraint intervenes.

(8) a. Whatwh did John say Mary gave twh to Bill?

b. * Whatwh0 did John say whowh1 Mary gave twh0 to twh1?(Wh-island violation)

c. * Whatwh0 did Bill think whichwh1 man twh1 says whowh1Mary gave twh0 to twh1?

d. * Whatwh0 did Sue claim whowh1 Bill thinks whichwh2 mantwh2 says Mary gave twh0 to twh1?

20

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MGs SMNF Locality & Grammar Size Conclusion

Taking Stock

É MGs are all about two structure-building operations:Merge and Move.

É Intermediate movement complicates formalism

É SMNF simplifies MGs, but at the risk of larger lexicons.

É Realistic grammars block the truly dangerous configurations.

É Unclear whether this is coincidence or conspiracy

21

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References

References I

Graf, Thomas, Alëna Aksënova, and Aniello De Santo. 2016. A single movement normal form forMinimalist grammars. In Formal Grammar : 20th and 21st International Conferences, FG 2015,Barcelona, Spain, August 2015, Revised Selected Papers. FG 2016, Bozen, Italy, August 2016,ed. Annie Foret, Glyn Morrill, Reinhard Muskens, Rainer Osswald, and Sylvain Pogodalla,200–215. Berlin, Heidelberg: Springer. URLhttps://doi.org/10.1007/978-3-662-53042-9_12.

Graf, Thomas, and Jeffrey Heinz. 2015. Commonality in disparity: The computational view ofsyntax and phonology. Slides of a talk given at GLOW 2015, April 18, Paris, France.

Graf, Thomas, James Monette, and Chong Zhang. 2017. Relative clauses as a benchmark forMinimalist parsing. Journal of Language Modelling 5:57–106. URLhttp://dx.doi.org/10.15398/jlm.v5i1.157.

Kobele, Gregory M. 2006. Generating copies: An investigation into structural identity in languageand grammar . Doctoral Dissertation, UCLA. URL http://home.uchicago.edu/~gkobele/files/Kobele06GeneratingCopies.pdf.

Stabler, Edward P. 1997. Derivational Minimalism. In Logical aspects of computational linguistics,ed. Christian Retoré, volume 1328 of Lecture Notes in Computer Science, 68–95. Berlin:Springer. URL https://doi.org/10.1007/BFb0052152.

Stabler, Edward P. 2011. Computational perspectives on Minimalism. In Oxford handbook oflinguistic Minimalism, ed. Cedric Boeckx, 617–643. Oxford: Oxford University Press.