A Psycholinguistically Motivated Version of TAGvera/TAGtalk.pdf · Visual world experiment:...
Transcript of A Psycholinguistically Motivated Version of TAGvera/TAGtalk.pdf · Visual world experiment:...
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A Psycholinguistically Motivated Version of TAG
Vera Demberg and Frank Keller
School of InformaticsThe University of Edinburgh
TAG+ 9 Workshop, June 7th 2008
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Motivation
Introduction
Motivation
To build a computational model of human sentence processing which ispsycholinguistically plausible
Model of comprehension (as opposed to production)
Assumptions: incrementality, connectedness, prediction
Goals
Better model of human sentence processing
Incremental processing beneficial for models of speech recognition,speech-to-speech translation
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Motivation
The Sentence Processing Model
The Model
Consists of a parsing process and a linking theory
Key requirements that the model should fulfill:Incrementality: Word-by-word processing, eager integration of each wordinto a syntactic structureConnectedness: Most strict version of incrementality – all words in asentence are connected into a single structurePrediction: Humans predict upcoming structure and lexemes beforeencountering them
The parser should therefore also implement these properties
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Motivation
Empirical Evidence for Incrementality and Prediction
Coordination processing: structural binding in c-command relation[Sturt & Lombardo 2005]
Experimental Findings: Incrementality & Connectedness
1 The pilot embarassed John and put himself in an awkward situation.
2 The pilot embarassed Mary and put herself in an awkward situation.
3 The pilot embarassed John and put him in an awkward situation.
4 The pilot embarassed Mary and put her in an awkward situation.
Gender default mismatch difficulty occurred at first pass reading on pronoun incondition (2) where herself is c-commanded by “pilot” but not in condition (4).
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Motivation
Empirical Evidence for Incrementality and Prediction
Visual world experiment: anticipatory eye-movements show that peoplepredict subsequent input [Kamide et al. 2003]
Experimental Findings: Incrementality and Prediction
“Der Hase frisst gleich den Kohl.”The Hare-nom will eat soon the cabbage-acc.
“Den Hasen frisst gleich der Fuchs.”The Hare-acc will eat soon the fox-nom.
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Motivation
Empirical Evidence for Incrementality and Prediction
Either...or processing:processing facilitation through prediction [Staub & Clifton 2002]
Experimental Findings: Prediction
either... orpresence of “either” leads to shorter fixation times on “or” and the secondconjunct
general treatment of two-part constructions [Cristea & Webber, 1997]
syntactic parallelismSecond conjunct processed faster if internal structure identical to firstconjunct [Frazier et al., 2000]
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Motivation
Choice of Grammar Formalism
Chose LTAG as a basis
Extended domain of locality gives powerful tool for implementingprediction (so phrase-structure grammar not suitable)
Incrementality and Connectedness easier to realize than with e.g. CCGno incremental derivation for object relative clauses in CCGincrementality in coordination problematic in CCG
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Motivation
Overview
1 Related Work
2 Relationship between Incrementality and Prediction
3 Lexicon Induction
4 Linking Theory for a Model of Sentence Processing
5 Summary
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Related Work
1 Related Work
2 Relationship between Incrementality and Prediction
3 Lexicon Induction
4 Linking Theory for a Model of Sentence Processing
5 Summary
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Related Work
Other versions of Incremental TAG / Incremental Parsers
Previous work on incremental TAG parsing:
Spinal LTAG [Shen and Joshi, 2005]not connectedno subcategorisation information
Incremental TAG [Kato et al., 2004]trees are leftmost expandedno proper modifier / argument distinction
Dynamic Version of TAG [Mazzei et al., 2007]most similar, but different grain sizes for prediction
Previous work on incremental parsing:
PCFG [Roark, 2001]
Dependency Parser [Nivre, 2004]
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Relationship between Incrementality and Prediction
1 Related Work
2 Relationship between Incrementality and Prediction
3 Lexicon Induction
4 Linking Theory for a Model of Sentence Processing
5 Summary
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Relationship between Incrementality and Prediction
The Interplay of Connectivity and Prediction
Connectivity and Prediction interact closely
We need a fully connected structure in order to determine what’s predicted(i.e. what is expected in order to build a grammatical sentence?)
We need prediction in order to achieve connectivity
Example of how parsing process works in PLTAG for sentence “The horseseldom won a prize”.
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Relationship between Incrementality and Prediction
The Interplay of Connectivity and Prediction (2)
Normal LTAG does not allow for connectedness
seldom
ADVP VP*
VP
NP
The horse
?
Elementary trees cannot always be connected directly
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Relationship between Incrementality and Prediction
The Interplay of Connectivity and Prediction (3)
Extend lexicon to include predictive entries
seldom
ADVP VP*
VP
The horse
NP
substitutionNP
S
VPadjunction
predictive lexicon entry
Insert connecting structure
Predicted nodes marked by “↓”
Structure is non-lexicalised “predictive lexicon entry”
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Relationship between Incrementality and Prediction
The Interplay of Connectivity and Prediction (4)
Introduce a Verification step
The horse
NP
S
VP
unification
seldom
ADVP
VP
S
NP VP
NPV
won
Observe accessibility constraints and dominance relations
Valid sentence analyses must not contain any open predicted nodeswhich have not been verified
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Relationship between Incrementality and Prediction
Prediction from the Lexicon
Substitution nodes that are to the right of a lexical anchor of an elementarytree typically generate predictions.
S
NP
DET
The
NN
horse
VP
ADVP
seldom
VP
V
won
NP↓
Exploit extended domain of locality to design lexicon entries in order tomodel psycholinguistic findings such as prediction in “either...or”constructions.
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Relationship between Incrementality and Prediction
Extended domain of locality
NP↓
DT
either
NP↓ CC↓
or↓
NP↓
S↓
DT
either
S↓ CC↓
or↓
S↓
(a) lexicon entry for either
S
NP
Peter
VP
V
read
NP↓
DT
either
NP
a book
CC↓
or↓
NP↓
(b) with either
NP
NP* CC
or
NP↓
S
S* CC
or
S↓
(c) lexicon entries for or
S
NP
Peter
VP
V
read
NP
a book(d) no either
Figure: Example for the use of TAG’s extended domain of locality to model expressionsVera Demberg (Edinburgh) PLTAG June 7th, 2008 17 / 34
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Lexicon Induction
1 Related Work
2 Relationship between Incrementality and Prediction
3 Lexicon Induction
4 Linking Theory for a Model of Sentence Processing
5 Summary
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Lexicon Induction
Determining Elementary Trees
Converted Penn Treebank into TAG format [Xia et al. 2000]Head percolation table for determining how to cut up the tree intoelementary trees [Magerman 1994]PropBank [Palmer et al. 2003] and NomBank [Vadas & Curran 2007] fordiscriminating arguments and modifiersDetermine domain of locality (e.g. either... or, pick... up)
vote
V
VPNP
NP
S
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
example lexiconentry generatedfrom tree
Figure: Generating lexicon entries from the Penn Treebank for an example sentence
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
NP
S
VP
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
DET
NP
N
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
Berlusconi
NPoften
ADVP
VP
SS
none
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
vote
V
VP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
DET
NP
N
S
people
N
NP
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
DET
NP
N
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
DET
NP
NBerlusconi
NP
no new predictive entry
SS
Berlusconi
NP
vote
V
VP
vote
V
VP
often
ADVP
VP
often
ADVP
VP
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
DET
NP
N
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
Berlusconi
NPoften
ADVP
VP
SS
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
DET
NP
Nvote
V
VP
vote
V
VP
Berlusconi
NP
Berlusconi
NP
NP
S
VP
SS
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Lexicon Induction
Connection Paths
Connection path: minimal amount of structure that is needed to connectwords w1 · · ·wn under one node [Lombardo & Sturt, 2002]
Determine predicted structures necessary by connectivity
predictive lexiconentries generatedfrom tree
NP
S
VP
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
The
DET
Italian
ADJ
N
people
N
NP
vote
V
VP
Berlusconi
NPoften
ADVP
VP
S
DET
NP
N
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Linking Theory for a Model of Sentence Processing
1 Related Work
2 Relationship between Incrementality and Prediction
3 Lexicon Induction
4 Linking Theory for a Model of Sentence Processing
5 Summary
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Linking Theory for a Model of Sentence Processing
The Linking Theory
Given an incremental TAG parser, we can analyse sentence, and knowwhere e.g. a lot of prediction is required, where a lot of nodes need to bematched up etc.
To predict processing difficulty, need to correlate the processes of theparser to some measure of difficulty
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Linking Theory for a Model of Sentence Processing
Ingredients of Linking Theory
Basic effects that we want to capture, and which have previously been shownto be significant predictors of reading time [e.g. Gibson, 1998; Hale, 2001;Lewis & Vasishth, 2005; Demberg & Keller, 2007]
Surprisal effectsunexpected material is more difficult to process than predicted material
Locality effectslong distance relationships as in center embedding are more difficult toprocess
Activation and Memory effectssyntactic rules and lexical items are easier to process when they have ahigh activation level
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Linking Theory for a Model of Sentence Processing
Linking the Parsing Process to Sentence ProcessingDifficulty (work in progress)
At each stage, we have a set of expectations E of what is needed to builda grammatical sentence (incrementality with full connectivity)Ranked parallelism : the expectations are weighted according to theProbability P(e) of the analysis that generated themExpectations have a timestamp t for when they were generated or lastactivated, and are held in memoryDecay f : the longer ago an expectation was generated, the more difficultit is to retrieve it; reflects the activation levelVerification causes processing difficulty when
1 Expectation satisfied (noun expected – noun found) Ei2 Analysis that generated expectation incompatible with new input Ed
processing difficulty Dw ∝ ∑e∈Ei
f (1
P(e))+ ∑
e∈Ed
f (P(e)) (1)
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Linking Theory for a Model of Sentence Processing
An example
S
NP VP
NPV
XP
NP
who_i
t_i
predictedstructure
N
grand−parents
N P=0.00003(ORC)
S predictedstructure
VP
t_i
NP
V
N
grand−parents
N
XP
NP
who_i
P=0.0004(SRC)
time
NPP=0.08(noun)
V
NP
NP
VP
S
time
P=0.002(verb)
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Linking Theory for a Model of Sentence Processing
An example
S predictedstructure
VP
t_i
NP
V
N
grand−parents
N
XP
NP
who_i
P=0.0004(SRC)
V
NP
NP
VP
S
time
P=0.002(verb)
S
NP VP
NPV
XP
NP
who_i
t_i
predictedstructure
N
grand−parents
N P=0.00003(ORC)
time
NPP=0.08(noun)
1 integrationtimestamp: 1 time unit timestamp: 1 time unit
4 integrations
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Linking Theory for a Model of Sentence Processing
An example
N
grand−parents
N
XP
NP
who_i
V
NP
NP
VP
S
P=0.0004
P=0.002(verb)
time
t_i
(SRC)
V
NP VP
S
helped
NP
XP
NP
who_i
S
time
NP
t_i
NPV
predictedstructure
VP
P=0.08
P=0.00003(ORC)
N
grand−parents
N
5 integrations:4 @ timestamp 21 @ timestamp 1
compare: integration at verbin SRC was 4 @ timestamp 1
NP
eggs
1 integrationtimestamp 2
integration at ORC noun was1 @ timestamp 1
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Summary
1 Related Work
2 Relationship between Incrementality and Prediction
3 Lexicon Induction
4 Linking Theory for a Model of Sentence Processing
5 Summary
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Summary
Conclusions
Summary
Motivated why it is interesting to model incrementality with fullconnectivity, and prediction based on recent findings in psycholinguistics
Suggested an incremental TAG version that implements theserequirements
Proposed a linking theory that relates the parsing process to a theory ofhuman sentence processing
Future Work
Define a probability model for PLTAG
Implement an incremental parser
Evaluation of processing difficulty predictions on eye-tracking corpus andcomparison to other theories of sentence processing
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Summary
Thanks for your attention!
QUESTIONS?
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Discussion on Grammar Formalisms
TAG vs. CCG – Incrementality
CCG is less easily incrementalizable than TAG.
Coordination [Lombardo & Sturt, 2005]
Example
the pilot embarrassed Mary and put herself in an awkward situation
S/(S\NP) S\NP CONJ (S\NP)/PP PP>
S\NP<Φ>
S\NP>B
S
Figure: Binding would only occur after full processing of second conjunct according toCCG derivation. However, the empirical finding is that humans experience difficulty ofgender mismatch as soon as they hit the reflexive pronoun.
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Discussion on Grammar Formalisms
TAG vs. CCG – Incrementality
CCG is less easily incrementalizable than TAG.
Object Relative Clauses
Example
the woman that every man saw
NP/N N (N\N)/(S/NP) NP/N N (S\NP)/NP>T >
N/(N\N) NP>B >T
NP/(N\N) S/(S\NP)>B B
NP/(S/NP) (S/NP)/((S\NP)/NP)>B
NP/((S\NP)/NP)>
NP
Figure: Example of incrementalized derivation for object relative clause in CCG. It isnot possible to make a fully incremental version inside the ORC NP “every man”.
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Discussion on Grammar Formalisms
TAG vs. CCG – Symmetry in Modification
Pre- and post-modification in CCG
NP
DT N↓
+ N
ADJ N*
→ NP
DT N
ADJ N↓
+ N
(a) TAG pre-modification
NP/N N/N N>B
NP/N>
NP(b) CCG pre-modification
NP
DT N
+ N
N* ADJ
→ NP
DT N
N ADJ(c) TAG post-modification
NP/N N N\N>T
N/(N\N)>B
NP/(N\N)>
NP(d) CCG post-modification
Figure: Comparision of pre- and post-modification in TAG and CCG
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Discussion on Grammar Formalisms
TAG vs. PCFG
Phrase structure Grammar
Standard arc-eager parsing completes all rules when the last instance ofthe innermost rule is foundVP → V • PPPP → P • NPNP → DT • N
Composition in parsing process would be necessaryVP → V [ P [DT • N] ] [Thompson et al. 1991]
Same amount of prediction would be needed
No extended domain of locality
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