Incremental syntactic prediction in the comprehension ...

2015-11-04

Incremental syntactic prediction in the comprehension of Swedish Thomas Hörberg Department of Linguistics, Stockholm University

Outline • Grammatical relations in Swedish transitive sentences from a corpusdistributional and psycholinguistic perspective • Assumptions & background • Corpus-based model of incremental argument interpretation • Experimental test of model predictions

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/ Thomas Hörberg, Department of Linguistics

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Assumptions – Grammatical Relations •

express argument functions (actor/undergoer, topic/focus)

•

grammatical encoding conditioned on prominence Animacy:

human < animate < inanimate

Person:

first, second < third

(e.g. Silverstein 1976)

Referentiality:

pronoun < proper name < common noun

Definiteness:

definite < specific indefinite < unspecific indefinite prototypical

prototypical

subject

object

•

subject > object in prominence

•

Exceptions (i.e. object > subject) typologically marked and infrequent in discourse

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Assumptions – Argument interpretation •

Assignment of argument functions

•

Highly incremental process that is probabilistic and frequency-driven, i.e.

(Actor / Undergoer)

draws upon statistical regularities in the input

/ determining W.O.

(e.g. Levy 2008; MacDonald &

Seidenberg 2006)

•

Morphosyntactic, prominence based and verb semantic information serve as Argument Interpretation Cues

The competition model (MacWhinney & Bates 1989)

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eADM (Bornkessel-Schlesewsky & Schlesewsky 2009; 2013)


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Sentence properties • Potentially ambiguous SVO / OVS word order • Case marking on personal pronouns only [DeNP1.NOM] har [vapen som väl matchar alla övriga nationers i regionenNP2.AMB] ”[TheyNP1.NOM] have [weapons that match those of other nations in the regionNP2.AMB]” ”[Gallant FloweringNP1.AMB] varnade [viNP2.NOM] för kraftigt” ”[Gallant FloweringNP1.AMB] [weNP2.NOM] warned about heavily [En härligt örtkryddad soppaNP1.AMB] käkade [viNP2.NOM] bland annat ”[A wonderful soup spiced with herbsNP1.AMB] [weNP2.NOM] ate among other things”

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NP1 + NP2 properties Animacy:

animate vs. inanimate

Givenness:

given vs. new

Definiteness:

definite vs. indefinite

Number:

singular vs. plural

Egophoricity:

1st / 2nd vs. 3rd person

Pronominality:

pronoun vs. noun

Case:

unmarked vs. nominative vs. accusative

Text deixis:

text deictic vs. non-deictic

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Verb semantic properties • verbs assign role semantic properties to NP arguments, i.e. “degree of Actorhood”

(e.g. Dowty 1991, Primus 2006)

• general verb semantic “entailments” and their interactions with prominence features therefore included Dowty (1991) Actor Undergoer Volitional Undergoes involvement change of state Sentience Cause event or Incremental theme change of state Causally affected Movement Stationary -

Category Volitionality Experiencer Causation Possession

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Primus (2006) Control – volitionality and intentionality Sentience Physical involvement Possession


Corpus properties Svensk Trädbank: balanced written Swedish texts

Corpus

SUC

TB

Genre Press: reportage Press: Editorial Press: Reviews Skills, Trades and Hobbies Popular Lore Belles Letters, Biography, Memoirs Miscellaneous Learned and Scientific Writing General fiction Mysteries and Science fiction Light reading Humor Brochure texts Newspaper texts Educational texts Debate articles

N texts 44 17 27 58 48 26 70 83 82 19 20 6 25 28 14 18

N sentences 7278 2385 3961 8933 6525 3598 10847 9633 13028 4070 2908 1071 1733 1669 1624 1134

N words 106079 40887 66002 134947 109665 61297 163333 192827 191507 45321 46126 14428 23122 24125 25623 23476

N hits 1495 473 712 1840 1503 805 1540 1809 3110 826 749 248 390 361 374 316

SVO sentences: 15679 OVS sentences: 872 2015-11-04


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Model of incremental argument interpretation •

Models the on-line change in the expectation of OVS (i.e. suprisal, Levy 2008)

•

given AICs provided by constituents over time

Suprisal of OVS modeled in terms of relative entropy between p(OVS | Ci) and p(OVS | Ci-1), i.e. the Kullback–Leibler divergence: DKL(p(OVS | Ci) || p(OVS | Ci-1)) = ∑jlog(p(OVS | Ci)j/ p(OVS | Ci-1)j) p(OVS | Ci)j

•

Based upon (penalized) logistic regression estimates of p(OVS | Ci) at constituents / time points Ci - Baseline model: p(OVS) - NP1 model: p(OVS | NP1) - NP1 + verb model: p(OVS | NP1, Verb) - Full model: p(OVS | NP1, Verb, NP2)

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Model of incremental argument interpretation Probabilities

Suprisals

baseline model: p(OVS): ~0.05

Suprisal NP1

DKL(p(OVS | NP1) | | p(OVS))

NP1 model: p(OVS | NP1)

Suprisal verb

NP1 + verb model:

DKL(p(OVS | NP1 + verb) | | p(OVS | NP1))

p(OVS | NP1 + verb)

Suprisal NP2

full model:

DKL(p(OVS | NP1 + verb + NP2) | | p(OVS | NP1 + verb))

p(OVS | NP1 + verb + NP2)

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Suprisal of upcoming constituents DKL = 0.06

[De

NP1]

They

”[They

DKL = 0.00 DKL = 0.00

[harverb] [vapen som väl matchar alla övriga nationers i regionen weapons that match those of other nations in the region

have NP1]

[have

verb]

DKL = 0.02

[Gallant Flowering Gallant Flowering

”[Gallant Flowering

[weapons that match those of other nations in the region

NP1]

NP1]

DKL = 0.00

[varnade warned

[we

NP2]

DKL = 0.34

[En härligt örtkryddad soppa A wonderful soup spiced with herbs

[warned

NP1]

”[A wonderful soup spiced with herbs

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verb]

we

verb]

NP2]

för kraftigt

p=.88

about heavily

about heavily”

DKL = 0.72 ate

[we

NP2]”

DKL = 5.49

[vi

[käkade

NP1]

NP2] p.999

among other things

among other things”


Suprisal at NP1 • Lexical NP1 • Some suprisal for inanimate / text deictic NP1

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Suprisal at verb • Lexical NP1 • Moderate suprisal for Inanimate & Volitional / Causative + Experiencer

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Suprisal at NP2

(disambiguation towards OVS)

• Lexical NP1 • case marked NP2 that disambiguates sentence towards OVS

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Testing the model

•

Testing the strongest model predictions

•

Self-paced reading

•

Reading times assumed to reflect processing

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Self paced reading

###### ####### ### #### ### #

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#######


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Self paced reading

Bollen ####### ### #### ### #

#######

ball.the

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Self paced reading

###### sparkar ### #### ### #

#######

kick

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Self paced reading

###### ####### han #### ### #

#######

he

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Self paced reading

###### ####### ### mitt ### #

#######

middle

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Self paced reading

###### ####### ### #### upp #

#######

up

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Self paced reading

###### ####### ### #### ### i

#######

in

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Self paced reading

###### ####### ### #### ### #

krysset top.corner.the

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Self paced reading

Bollen sparkar han mitt upp i

krysset

ball.the

top.corner.the

kick

he

middle up

in

”The ball he kicks right up into the top corner” • Dependent variable: time latency between button presses • Analyses done on region RTs rather than word RTs • Task: Comprehension question following each sentence: e.g. ”Does he kick the ball right up into the top corner?”

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Predictions – sentence differences Word order

Verb class

Object animacy

Example

Inanimate

Bollen sparkar han mitt upp i krysset

Animate

Killen sparkar han mitt på smalbenet

Inanimate

Bollen glömmer han mitt på fotbollsplanen

Animate

Killen glömmer han sent på kvällen

Inanimate

Han sparkar bollen mitt upp i krysset

Animate

Han sparkar killen mitt på smalbenet

Inanimate

Han glömmer bollen mitt på fotbollsplanen

Animate

Han glömmer killen sent på kvällen

”The ball he kicks right up in the middle of the top corner”

Volitional ”The guy he kick on the middle of the shin”

OVS ”The ball he forgets in the middle of the football field”

Experiencer ”The guy he forgets late at night”

”He kicks the ball up in the middle of the top corner”

Volitional ”He kicks the guy on the middle of the shin”

SVO ”He forgets the ball in the middle of the football field”

Experiencer ”He forgets the guy late at night”

Regions • e.g. /Bollen sparkar

reg.1

/ jag mitt

reg.2

/ upp i

reg.3

/ krysset

FW

/

• Region 1 RTs correspond to verb suprisal, region 2 RTs to NP2 suprisal

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Results – RT differences

** **

• Faster RTs in SVO sentences vs. OVS sentences • Faster RTs for animate vs. inanimate NP1 at region 1 in OVS experiencer verb sentences • Slower RTs for animate vs. inanimate NP1 at region 2 in OVS volitional verb sentences • No significant RT differences in SVO sentences 2015-11-04


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Conclusions

• Animacy functions as a cue in argument interpretation that interacts with verb class • Distribution of prominence cues in discourse predicts reading times

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References Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2009). The role of prominence information in the real-time comprehension of transitive constructions: A cross-linguistic approach. Language and Linguistics Compass, 3(1), 19-58. Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2013). Reconciling time, space and function: A new dorsal–ventral stream model of sentence comprehension. Brain and Language, 125, 60-76. Dowty, D. (1991). Thematic Protoroles-Roles and Argument Selection. Language, 67(3). Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3), 1126-1177. MacDonald, M. C., & Seidenberg, M. S. (2006). Constraint satisfaction accounts of lexical andsentence comprehension in M. Traxler and M. A. Gernsbacher (Eds.) Handbook of Psycholinguistics (pp. 581-610). New York, NY: Academic Press). MacWhinney, B., & Bates, E. (1989). Functionalism and the Competition Model. In B. MacWhinney & E. Bates (Eds.), The Crosslinguistic Study of Sentence Processing (pp. 3-73). Cambridge: Cambridge University Press. Primus, B. (2006). Mismatches in semantic-role hierarchies and the dimensions of role semantics. In I. Bornkessel, M. Schlesewsky, B. Comrie, & A. D. Friederici (Eds.), Semantic Role Universals and Argument Linking:Theoretical, Typological and Psycholinguistic Perspectives (pp. 53-89). Berlin: Mouton de Gruyter. Silverstein, M. (1976). Hierarchy of features and ergativity. In R. M. W. Dixon (Ed.), Grammatical Categories in Australian Languages (pp. 112-171). New Jersey: Humanities Press.

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