Computational Models of Discourse and
Dialogue 2011: Conversation in Social Media
Natural Language and Dialogue Systems Lab
Persuasion in Social Media
 Persuasion and argumentation in social media
websites and forums
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NLDS Social Media Dialogue Data
 Data collected in the last year in collaboration with
FoxTree’s Lab & Anand’s SemLab
 Convinceme.net
 4forums.org
 Carm.org
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Using Mechanical Turk to get labels
 http://pcon.soe.ucsc.edu/mturk_external/123/123.ph
p?pageId=1597&assignmentId=ASSIGNMENT_ID_N
OT_AVAILABLE&hitId=1HNBWKACQBSEV0YDIO
YSBWM1C0YNIP
 http://pcon.soe.ucsc.edu/mturk_external/qr/qr.php?p
ageId=1398&assignmentId=ASSIGNMENT_ID_NOT
_AVAILABLE&hitId=1CEJFP6T9BRSEF7QNPYEV9U3
7T7Y6W
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Classic Models of Discourse and Dialogue
Structure
(Task Oriented Dialog, Newspaper texts)
Marilyn Walker. CS245. April 1st, 2010
Natural Language and Dialogue Systems Lab
Dialogue Processing (circa 1988)
 Grosz & Sidner 1986
 Planning, Grice
 Mann & Thompson 1988
 Rhetorical Relations,
Text Structure
 Polanyi 1984
 Linguistic Discourse
Model
 Hobbs 1979
 Coherence Relations
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Dialogue Processing (circa 1988)
 Me 1989
 Starting my Ph.D.
with Aravind Joshi
and Ellen Prince
 Science IS NOT a
belief system
 => Empirical
Methods in
Discourse
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Empirical/Statistical Approaches in NLP
 Penn Treebank first available ~ 1990
 Plenty of data for parsing and POS
 But what about language behavior above the sentence?
 What about interactive language?
 1993: NSF Workshop on Centering in Naturally
Occurring Discourse => Walker, Joshi & Prince 1997
 1995: AAAI Workshop on Empirical Methods in
Discourse => Walker & Moore CL special issue
 1996: NSF Workshop on Discourse & Dialogue Tagging
=> DAMSL markup
 NOW: there is virtually no work in NLP on discourse
and dialogue that is not corpus based/empirical.
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What is a dialogue model?
 A model is an abstraction of a thing, simplified or
dimensionally reduced
 A good model should be simpler but capture the
essence of the real thing.
 A good dialogue model should be testable. It should
make predictions. Its claims should be such that one
should be able to prove whether or not it is correct.
 A good dialogue model should lead to results that
are more generalizable.
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Dialogue Structure
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What makes a text coherent?
What are discourse structures?
Theories of discourse structures
Approaches to build discourse structures
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Discourse Coherence
 Example:
 (1) John hid Bill’s car keys.
 (2) He was drunk.
 (1) John hid Bill’s car keys.
 (2) He likes junk food.
 (1) George Bush supports big business.
 (2) He’s sure to veto House Bill 1711.
 Hearers try to find connections between utterances in a discourse.
 The possible connections between utterances can be specified as a set of
coherence relations.
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Coherence relations (Hobbs,1979)
 Result: S0 causes S1
 John bought an Acura. His father went ballistic.
 Explanation: S1 causes S0.
 John hid Bill’s car keys. He was drunk.
 Parallel: S0 and S1 are parallel.
 John bought an Acura. Bill bought a BMW.
 Elaboration: S1 is an elaboration of S0.
 John bought an Acura this weekend. He purchased it for
$40 thousand dollars.
 …
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Discourse structure
S1: John took a train to Bill’s
car dealership.
S2: He needed to buy a car.
S3: The company he works for
now isn’t near any public
transportation.
S4:He also wanted to talk to
Bill about their softball
leagues.
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]
Explanation
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Discourse structure
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]
Explanation
S1: John took a train to Bill’s
car dealership.
S2: He needed to buy a car.
S3: The company he works for
now isn’t near any public
transportation.
S4:He also wanted to talk to
Bill about their softball
leagues.
]
Parallel
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Discourse structure
]
Explanation
]
Parallel
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]
Explanation
S1: John took a train to Bill’s
car dealership.
S2: He needed to buy a car.
S3: The company he works for
now isn’t near any public
transportation.
S4:He also wanted to talk to
Bill about their softball
leagues.
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Discourse parsing
Explanation (e1)
S1 (e1)
Parallel (e2;e4)
Explanation (e2)
S2(e2)
S4 (e4)
S3(e3)
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Why compute discourse structure?

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Natural language understanding
Summarization
Information retrieval
Natural language Generation
Reference resolution
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Theories of discourse structure
 Mann and Thompson’s Rhetorical structure theory
(1988)
 Grosz and Sidner’s Attention, intention and
structure of discourse (1986)
 Discourse TAG. Penn Discourse Treebank (PDTB)
 We will read a lot of papers using DTAG and PDTB
so am just going to talk about these ‘classic theories’
today.
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Rhetorical structure theory (RST)
 Mann and Thompson (1988)
 One theory of discourse structure, based on
identifying relations between parts of the text:
 Defined 20+ rhetorical relations
 Presentational relations: intentional
 Subject matter relations: informational
 Nucleus: central segment of text
 Satellite: more peripheral segment
 Relation definitions and more.
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Presentational (intentional) relations
 Those whose intended effect is to increase some
inclination in the hearer.
 Relations:
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Antithesis
Background
Concession
Enablement:
Evidence
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- Justify
- Motivation
- Preparation
- Restatement
- Summary
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Subject matter (information) relations
 Those whose intended effect is that the hearer recognize the
relation in question.
 Relations
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Circumstance
Condition
Elaboration
Evaluation
Interpretation
Means
Non-volitional cause
Non-volitional result
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- Otherwise
- Purpose
- Solutionhood
- Unconditional
- Unless
- Volitional cause
- Volitional result
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Multinuclear relations
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Contrast
Joint
List
Multinuclear restatement
Sequence
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Some examples
 Explanation: John went to the coffee shop. He was
sleepy.
 Elaboration: John likes coffee. He drinks it every day.
 Contrast: John likes coffee. Mary hates it.
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Discourse structure
John likes coffee
They argue a lot
Mary hates coffee.
He drinks it every day
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A relation: Evidence
 (a) George Bush supports big business.
 (b) He’s sure to veto House Bill 1711.
 Relation Name: Evidence
 Constraints on Nucl: H might not believe Nucl to a
degree satisfactory to S.
 Constraints on Sat: H believes Sat or will find it
credible
 Constraints on Nucl+Sat: H’s comprehending Sat in
Sat increases H’s belief of Nucl.
 Effect: H’s belief of Nucl is increased.
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A relation: Volitional-Cause
 (a) George Bush supports big business.
 (b) He’s sure to veto House Bill 1711.
Relation Name: Volitional-Cause
Constraints on Nucl: presents a volitional action
Constraints on Sat: none.
Constraints on Nucl+Sat: Sat presents a situation that could
have caused the agent of the volitional action in Nucl to
perform the action.
 Effect: H recognizes the situation presented in Sat as a cause
for the volitional action presented in Nucl.




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Another example
S: (a) Come home by 5:00. (b) Then we can go to the
hardware store before it closes. (c) That way we can
finish the bookshelves tonight.
(a)
(a)
motivation
motivation
(b)
(b)
condition
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(c)
(c)
condition
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A Problem with RST
(Moore & Pollack, 1992)
 How many rhetorical relations are there?
 How can we use RST in dialogues?
 How do we incorporate speaker intentions into
RST?
 RST does not allow for multiple relations between
parts of a discourse: informational and intentional
levels must coexist.
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Grosz & Sidner (1986)
Natural Language and Dialogue Systems Lab
Grosz and Sidner (1986)
 Three components:
 Linguistic structure
 Intentional structure
 Attentional state
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Linguistic structure
 The structure of the sequence of utterances that
comprises a discourse.
 Utterances form Discourse Segment (DS); and a
discourse is made up of embedded DSs.
 What exactly is a DS?
 Any evidence that humans naturally recognize segment
boundaries?
 Do humans agree on segment boundaries?
 How to find the boundaries automatically?
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Intentional structure
 Speakers in a discourse may have many intentions:
public or private.
 Discourse purpose (DP): the intention that
underlies engaging in a discourse.
 Discourse segment purpose (DSP): the purpose a
DS. How this segment contributes to achieving the
overall DP?
 Two relations between DSPs:
 Dominance: if DSP1 contributes to DSP2, we say DSP2
dominates DSP1.
 Satisfaction-precedence: DSP1 must be satisfied before
DSP2.
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Attentional State
 The attentional state is an abstraction of the
participants’ focus of attention as their discourse
unfolds.
 The state is a stack of focus spaces.
 A focus space (FS) is associated with a DS, and it
contains DSP and objects, properties, and relations
salient in the DS.
 When a DS ends, its FS is popped.
 When a DS starts, its FS is pushed onto the stack.
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An example
C1: I need to travel in May.
A1: And, what day in May do you want
to travel?
C2: I need to be there for a meeting on 15th.
A2: And you are flying into what city?
C3: Seattle.
A3: And what time would you like to
leave Pittsburgh?
C4: Hmm. I don’t think there are many
options for non-stop.
A4: There are three non-stops today.
C5: What are they?
….
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DS1
DS2
DS3
DS0
DS4
DS5
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Discourse structure with intention info
DS0


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


DS1
DS2
C1
A1-C2
DS3
DS4
DS5
A2-C3
A3
C4-C7
I0: C wants A to find a flight for C
I1: C wants A to know that C is traveling in May.
I2: A wants to know the departure date etc.
I3: A wants to know the destination
I4: A wants to know the departure time
I5: C wants A to find a nonstop flight
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Problems with G&S 1986
 Assume that discourses are task-oriented
 Assume there is a single, hierarchical structure
shared by speaker and hearer
 Do people really build such structures when they
speak? Do they use them in interpreting what others
say?
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Walker 1996: Limited Attention & Discourse
Structure
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LIMITED ATTENTION CONSTRAINT
Walker 1993, 1996
 ellipsis interpretation
 pronominal anaphora interpretation
 inference of discourse relations between utterances A
and B
 B MOTIVATES A
 B is EVIDENCE for A
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How is attention modeled ?
 Linear Recency
 Hierarchical Recency
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Centering
 Centering is formulated as a theory that relates focus
of attention, choice of referring expression, and perceived
coherence of utterances, within a discourse segment
[Grosz et al., 1995].
 Brennan, Walker & Pollard 1987: Centering theory of
Anaphora Resolution
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What about Processing & Centering?
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Informationally Redundant Utterances
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Centers cross segments
 Centers continued over discourse segment boundaries with
pronominal referring expressions whose form is identical to
those that occur within a discourse segment.
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(29) and he's going to take a pear or two, and then.. go on his way
(30) um but the little boy comes,
(31) and uh he doesn't want just a pear,
(32) he wants a whole basket.
(33) So he puts the bicycle down,
(34) and he..
[Pear Stories, Chafe, 1980; Passonneau, 1995]:
 => discourse segment boundary between (32) and (33).
[Passonneau, 1995, Passonneau & Litman 1997]
 [Walker et al., 1998], (33) realizes a CONTINUE transition,
indicating that utterance (33) is highly coherent in the context
of utterance (32).
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Why is centering only within Segment?
 It is not plausible that a different process than
centering would be required to explain the
relationship between utterances (32) and (33), simply
because these utterances span a discourse segment
boundary.
 Centering is a theory that relates focus of attention,
choice of referring expression, and perceived coherence
of utterances, within a discourse segment [Joshi &
Weinstein 1983, Grosz, Joshi & Weinstein, 1995],
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Cache Model (Human Working Memory)
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Building discourse structure
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Tasks
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Identify units, e.g. discourse segment boundaries
Determine relations between segments
Determine intentions of the segments
Determine the attentional state
 Methods:
 Inference-based approach: symbolic
 Cue-based approach: statistical
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Inference-based approach
 Ex: John hid Bill’s car keys. He was drunk.
 X is drunk  people do not want X to drive
 People don’t want X to drive  people hide X’s
car key.
 Abduction:
 


 AI-complete: Require and utilize world knowledge.
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Cue-based approach
 Attentional state:
 Attentional changes:
 (push) now, next, but, ….
 (pop) anyway, in any case, now back to, ok, fine,...
 True interruption: excuse me, I must interrupt
 Flashback: oops, I forgot
 Intention:
 Satisfaction-precedes: first, second, furthermore, ….
 Dominance: for example, first, second, ….
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Cues (cont)
 Linguistic structure
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Elaboration: for example, …
Concession: although
Condition: if
Sequence: and, first, second.
Contrast: and, …
…
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One example
 (Marcu 1999): Train a parser on a discourse
treebank.
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90 trees, hand-annotated for rhetorical relations (RR)
Learn to identify Elementary discourse units (EDUs)
Learn to identify N, S, and their relation.
Features: WordNet-based similarity, lexical, structural, …
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Results
 Identify units (Elementary DUs): 96%-98% accuracy
 Identify hierarchical structures (2 EDUs are related):
Recall=71%, Precision=84%
 Identify nucleus/satellite labels: Rec=58%, Prec=69%
 Identify rhetorical relation: Rec=38%, Prec=45%
Hierarchical structure is easier to id than rhetorical
relations.
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Discourse Representation Theory
Natural Language and Dialogue Systems Lab
Informational Components.
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Data
Participants
Beliefs
Common ground
Intentions
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Formal Representations
 Formal representation of informational
components
 Typed feature structures
 Lists
 Sets
 Propositions
 First order logic
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Dialog Moves
 Trigger the update of the information state
 Grammatical triggers
 External events
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Update Rules
 Govern information state updates
 Sometimes incorporates domain knowledge
 Sometimes govern behavior of dialog moves
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Control Strategy
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Decide which update rule applies
Simple priority list
Game theory
Utility theory
Statistical methods
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Also for Dialogue Systems…
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Dialog Theories
 Finite State Dialog Models
 Plan-based Models
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Finite State Dialog Models
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Information is a state in the FSM
Dialog moves are inputs matching transitions
Update Rules are FSM lookups and transitions
Control Strategy is static, the FSM itself
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Plan-based Models
 Information state is the modeled beliefs, desires, and
intentions of the participants
 Dialog moves are speech acts, e.g. request and inform
 Update rules are cognitive rules of evidence
 Control Strategies are classic AI plan-based
strategies
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What is a discourse relation? (Joshi,Prasad, Webber, Coling/ACL Tutorial 1996)
The meaning and coherence of a discourse results partly from how its
constituents relate to each other.
 Reference relations
 Discourse relations
Discourse Coherence
Reference Relations
Discourse Relations
Informational
Intentional
63
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Why Discourse Relations?
Informational discourse relations convey relations that hold in
the subject matter.
Intentional discourse relations specify how intended discourse
effects relate to each other.
[Moore & Pollack, 1992] argue that discourse analysis requires both
types.
RST informational or semantic relations (e.g, CONTRAST,
CAUSE, CONDITIONAL, TEMPORAL, etc.) between abstract
entities of appropriate sorts (e.g., facts, beliefs, eventualities,
etc.), commonly called Abstract Objects (AOs) [Asher, 1993].
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Why Discourse Relations?
Discourse relations provide a level of description that is
 theoretically interesting, linking sentences (clauses) and
discourse;
 identifiable more or less reliably on a sufficiently large
scale;
 capable of supporting a level of inference potentially
relevant to many NLP applications.
65
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How are Discourse Relations declared?
Broadly, there are two ways of specifying discourse relations:
Abstract specification
 Relations between two given Abstract Objects are always inferred, and
declared by choosing from a pre-defined set of abstract categories.
Lexical elements can serve as partial, ambiguous evidence for inference.
Lexically grounded
 Relations can be grounded in lexical elements.
 Where lexical elements are absent, relations may be inferred.
66
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Rhetorical Structure Theory (RST)
RST [Mann & Thompson, 1988] associate discourse relations with
discourse structure (TEXT).
 Discourse structure reflects context-free rules called
schemas.
 Applied to a text, schemas define a tree structure in which:
• Each leaf is an elementary discourse unit (a continuous text span);
• Each non-terminal covers a contiguous, non-overlapping text span;
• The root projects to a complete, non-overlapping cover of the text;
• Discourse relations (aka rhetorical relations) hold only between daughters
of the same non-terminal node.
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Types of Schemas in RST
RST schemas differ with respect to:
 what rhetorical relation, if any, hold between right-hand side (RHS) sisters;
 whether or not the RHS has a head (called a nucleus);
 whether or not the schema has binary, ternary, or arbitrary branching.
RST schema types in RST annotation
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Moore & Pollack 1992
 Example 1
 (a) George Bush supports big business.
 (b) He's sure to veto House Bill 1711.
SATELLITE
NUCLEUS
 Relation name: EVIDENCE (MT 1987)
 Evidence is a “presentational relation”
 Constraints on Nucleus: H might not believe Nucleus to a
degree satisfactory to S.
 Constraints on Satellite: H believes Satellite or will find it
credible.
 Constraints on Nucleus + Satellite combination: H's
comprehending Satellite increases H's belief of Nucleus.
 Effect: H's belief of Nucleus is increased
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Moore & Pollack 1992
 Example 1
 (a) George Bush supports big business.
 (b) He's sure to veto House Bill 1711.
 Relation name: VOLITIONAL-CAUSE
 Volitional Cause is a “subject matter” relation
 Constraints on Nucleus: presents a volitional action or situation that
could have arisen from a volitional action.
 Constraints on Satellite: none.
 Constraints on Nucleus + Satellite combination: Satellite presents a
situation that could have caused the agent of the volitional action in
Nucleus to perform that action; without the presentation of Satellite, H
might not regard the action as motivated or know the particular
motivation; Nucleus is more central to S's purposes in putting forth the
Nucleus-Satellite combination than Satellite is.
 Effect: H recognizes the situation presented in Satellite as a cause for the
volitional action presented in Nucleus.
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Moore & Pollack 1992
 Presentational relations: == Speaker intention
 Speaker always has an INTENTION
 But Informational (subject matter relations) also
necessary to understand the discourse
 Multiple levels of analysis are simultaneously available
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