Semantic (or Thematic) Proto-Roles Drew Reisinger, Rachel Rudinger, Frank Ferraro, Craig Harman,...

Semantic (or Thematic)Proto-Roles

Drew Reisinger, Rachel Rudinger, Frank Ferraro, Craig Harman, Aaron White,

Kyle Rawlins, and Benjamin Van Durme

Talk Outline

Overview of thematic/semantic roles

Dowty (1991)’s proto-roles

New: A crowdsourced proto-role corpus

Future work

Thematic roles (linguistics)Semantic roles (comp. ling.)

Verbal meanings consist of information like:

Who did what to who(m)?What happened to which individual?

…Thematic roles fill in the who (and some of the what) Agent, Patient, Theme…

Fillmore 1968; Gruber 1965; Jackendoff 1972, 1976

Generalized Thematic Roles

Lexical entry

lemma: /h tɪ /category: V

role-list:AGENTTHEMEINSTRUMENT

Dowty 1991; Schlesinger 1995; Van Valin 1990, 1999; Croft 1998


Lexical entry



LINKING RULES



Lexical entry



LINKING RULES SUBJ hit DOBJ with OBLQ


PropBank (comp. ling.)

The Proposition Bank: An annotated corpus of semantic roles. Palmer, Gildea, and Kingsbury. Computational Linguistics 31.1 (2005): 71-106.

Role Fragmentation


…

Agent, Patient

Dowty 1991

Role Fragmentation


…

Agent, Patient, Theme, Beneficiary

Dowty 1991

Role Fragmentation


…

Agent, Patient, Theme, Beneficiary, Actor, Instrument, Co-Patient, Value

Dowty 1991

Role Fragmentation


…

Agent, Patient, Theme, Beneficiary, Actor, Instrument, Co-Patient, Value,

Item, Speaker, Difference, Message,Goods, Addressee, Sender, Donor, Seller,Cognizer, Co-Theme, Experiencer, Buyer,

…

Dowty 1991; Baker, Fillmore & Lowe 1998 (FrameNet)

Talk Outline




Future work

… then along came Dowty

Thematic proto-roles and argument selection. David Dowty. Language. 1991.

So many roles!

Dowty (1991)

for [roles to have] explicit semantic content, the meanings of all natural-language predicates … must permit us to assign the argument … to some official thematic role or other… it cannot … ‘fall in the cracks’ between roles

Dowty (1991)

This is a very strong empirical claim …and as soon as we try to be precise about exactly what Agent, Patient, etc., ‘mean’, it is all to subject to difficulties and apparent counterexamples

Dowty (1991)

we may have have a hard time pinning down the traditional role type because role types are simply not discrete categories at all

Roles = property configurations

Dowty argued for the notion of: proto-Agent and proto-Patient

Verb arguments only tend to have certain basic properties, and these correlate in Agent/Patient like ways

Arguments with more Agent properties tend to be SUBJECT, those with more Patient properties, OBJECT

Kyle Rawlins

More of a bridge might be helpful here. Something like: what are the ways in which the things in the previous few slides need improvement? Some ideas: * Problems on the theoretical side with generalized roles (led to PropBank Arg0, Arg1) * Useful to enrich the "what" aspect of role labels * Maybe something about the challenges you had in the project with meg? I.e. making it easier to rapidly get data

Dowty’s Properties

Argument Selection Principle

“In predicates with grammatical subject and object, the argument for which the predicate entails the greatest number of Proto-Agent properties will be lexicalized as the subject of the predicate; the argument having the greatest number of Proto-Patient entailments will be lexicalized as the direct object.”

Dowty (1991), p. 576


SUBJ hit DOBJwith OBLQ

HITTER HITTEE HIT-WITH

SYNTAX

SEMANTICS




SYNTAX

SEMANTICS

causes:+

exists:+

volitional:-

stationary:-

…

causes:-

exists:+

volitional:-

stationary:+

…

causes:-

exists:-

volitional:-

stationary:-

…




SYNTAX

SEMANTICS

p-Agent:3

p-Patient:0

p-Agent:1

p-Patient:1

p-Agent:1

p-Patient:0

Kako (2006)

Do normal people (student subjects) have stable judgments akin to Dowty’s?

Experiment with simple sentences,using nonce arguments

Thematic role properties of subjects and objects. Kako. Cognition 101.1 (2006): 1-42.

Kako (2006)


The rom found the zarg.How likely is it that the rom chose to be involved in finding?How likely is it that the rom moved?…


Kako (2006)


(subj. rating – obj. rating) ≈ measure of association between property and proto-Agent


Kako’s Findings

Talk Outline




Future work

For details, see

Semantic proto-roles.Drew Reisinger, Rachel Rudinger, Francis Ferraro, Craig Harman, Kyle Rawlins, Benjamin Van Durme. Transactions of the Association for Computational Linguistics 3 (2015): 475-488.

The neeglur .killed the bogrub

For :the bogrub

- How likely or unlikely is it that was/were altered or somehow changed during or by the end of the ?

the bogrub

killing

veryunlikely

somewhatunlikely

somewhatlikely

verylikely

not enoughinformation

The neeglur .killed the bogrub

For :the bogrub

- How likely or unlikely is it that was/were altered or somehow changed during or by the end of the ?

the bogrub

killing

veryunlikely

somewhatunlikely

somewhatlikely

verylikely

not enoughinformation

1 2 3 4 5

How likely or unlikely is it that …

Arg caused Pred to happen?Arg chose to be involved in the Pred?Arg was/were aware of being involved in the Pred?Arg was sentient?Arg changes location during Pred?Arg existed as a physical object?Arg existed before the Pred began?Arg existed during the Pred?Arg existed after the Pred stopped?Arg changed possession during the Pred?The Arg was/were altered or somehow changed during or by the end of the Pred?Arg was stationary during the Pred?



Instigated



Volitional



Moved

Kako (2006)lab setting,nonce sentences

JHU (2015)crowd sourced,nonce sentences

Let’s Build a Corpus!

Why?

Let’s Build a Corpus…

Why?

Extending Dowty requires broad data e.g. oblique arguments, alt. linking

rules

… of Naturalistic Data

Dowty concerned with verbal entailments

e.g. If x is a KILLER, then x is volitionally involved in the event

Our data: entailments of particular arguments in context

Mechanical Turk

Why?

Possible to factor out argument entailments

Why?


Expose counterexamples to default inferences

e.g. Mary accidentally killed her pet fish.

Why?


Expose counterexamples to default inferencese.g. Mary accidentally killed her pet fish.

Some morphosyntactic realizations depend on argument properties, e.g. DOM (Aissen 2003; Bossong 1991, 1998)

(re-)Annotate PropBank

~350 hours of annotator time

~10,000 unique arguments labeled

@ http://decomp.net

Kako (2006)Small scale,nonce sentences

JHU (2015)Large scale,real sentences

There now exists corpus-based evidence in support of Dowty’s Proto-

Role hypothesis

“Roles”

Each configuration of 11 responses = one “role”

~10,000 arguments labeled leads to ~800 unique “roles”

At least 100 of these configurations appear at least 10 times

Entailment Corner Cases


Typical killer: volitional, aware, sentient


Accidental killer: not volitional or aware


Atypical killer: not volitional, aware, sentient


Atypical killer: not volitional, aware, sentientEven independent existence might fail!

Verbal Entailments

The kill example shows how argument entailments constrain verbal entailments

Verbal Entailments

The kill example shows how argument entailments constrain verbal entailments

We factor out individual argument effects to estimate general property ratings for verbs

Argument Selection

Quantify how well a verb conforms to Dowty (1991)’s Argument Selection principle with the following score:

AgtSUBJ – AgtOBJ + PatOBJ – PatSUBJ

A verb is consistent with Dowty (1991) if the score is positive

Argument Selection

Argument Selection

Which verbs aredown here?

Verbs with Negative Scoresaccelerateadorn anger appease assemble beef bill blow bolster brave call call calm captain catch chain clear comprise

concern confirm cover crop define detect disappointdistort disturb double dust elevate embarrass employ employ exceed exclude feature

feed fill flatten follow force free freeze fuel galvanize halt hamstring haul haunt hire house hurt ignore illustrate

impress include inhibit involve justify keep lag last leave limit list lock merit mimic misstate move name outnumber

outpace outstrip phone pit poll preserve protect pursue puzzle rattle recover regain remember renew repel represent review rivet

scandalizescare sense set settle shake shield shock shroud shrug sign soil stun suggest surprise survey swamp swell

take target thrust top touch trouble turn underscoreunmask unnerve vent waste wed worry wreck yield

Talk Outline




Future work

Future Work

Morphosyntax: verifying and extending Grimm (2011)

JHU Decompositional Semantics Initiative

Applications to Morphosyntax

Grimm (2011): Each case corresponds to a connected region of the lattice of proto-role property configurations

Can we predict case alternations? Cross-linguistic case usage?

Semantics of case. Grimm. Morphology 21 (2011): 515-544.

The Johns HopkinsDecompositional Semantics Initiative

- Semantic Proto-Role Labeling (systems)

- Nominal semantics (factored word sense, …)

- Verbal semantics (general entailments)

- Constraints on lexical representation learning

- Connections to: Common Sense

Acknowledgements

DARPA LORELEI BAA-15-04 (Low resource event understanding)

NSF BCS-1344269 (Gradient Symbolic Computation)

JHU Science of Learning Institute

Benjamin Van Durme

Drew Reisinger

Kyle Rawlins

Rachel Rudinger Frank Ferraro Craig Harman

Questions?

http://decomp.net

Aaron White

Semantic (or Thematic) Proto-Roles Drew Reisinger, Rachel Rudinger, Frank Ferraro, Craig Harman,...

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