Computational Models of Discourse and Dialogue 2011: Conversation in Social Media

Natural Language and Dialogue Systems Lab

Computational Models of Discourse and Dialogue 2011: Conversation in Social Media

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LAB

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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

http://www.convinceme.net/

http://www.4forums.org/

http://www.carm.org/


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Using Mechanical Turk to get labels http://pcon.soe.ucsc.edu/mturk_external/1

23/123.php?pageId=1597&assignmentId=ASSIGNMENT_ID_NOT_AVAILABLE&hitId=1HNBWKACQBSEV0YDIOYSBWM1C0YNIP

http://pcon.soe.ucsc.edu/mturk_external/qr/qr.php?pageId=1398&assignmentId=ASSIGNMENT_ID_NOT_AVAILABLE&hitId=1CEJFP6T9BRSEF7QNPYEV9U37T7Y6W

http://pcon.soe.ucsc.edu/mturk_external/123/123.php?pageId=1597&assignmentId=ASSIGNMENT_ID_NOT_AVAILABLE&hitId=1HNBWKACQBSEV0YDIOYSBWM1C0YNIP









Classic Models of Discourse and Dialogue Structure(Task Oriented Dialog, Newspaper texts) Marilyn Walker. CS245. April 1st, 2010


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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

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.

] Explanation


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Discourse structure





] Explanation ] Parallel


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Discourse structure





] Explanation

]

Parallel] Explanation


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Discourse parsing

Explanation (e1)

S1 (e1) Parallel (e2;e4)

Explanation (e2) S4 (e4)

S2(e2) S3(e3)


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Why compute discourse structure?

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.

http://www.sfu.ca/rst/01intro/definitions.html


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Presentational (intentional) relations Those whose intended effect is to increase

some inclination in the hearer. Relations:

Antithesis - Justify Background - Motivation Concession - Preparation Enablement: - Restatement Evidence - Summary


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Subject matter (information) relations Those whose intended effect is that the hearer

recognize the relation in question. Relations

Circumstance - Otherwise Condition - Purpose Elaboration - Solutionhood Evaluation - Unconditional Interpretation - Unless Means - Volitional cause Non-volitional cause - Volitional result Non-volitional result


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Multinuclear relations 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

He drinks it every day

Mary hates coffee.

They argue a lot

elabo

ratio

n

contrast

cause


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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 exampleS: (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) (b) (c)(b) (c)

motivation motivation

condition 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.


Grosz & Sidner (1986)


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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?….

DS0

DS2

DS3

DS4

DS5

DS1


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Discourse structure with intention info

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

DS0

DS1 DS2 DS3 DS4 DS5

A1-C2 A2-C3 A3 C4-C7C1


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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?


Walker 1996: Limited Attention & Discourse Structure


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LIMITED ATTENTION CONSTRAINTWalker 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. (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)


Building discourse structure


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Tasks 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

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. 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.


Discourse Representation Theory


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Informational Components. 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 Decide which update rule applies Simple priority list Game theory Utility theory Statistical methods


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 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

Reference Relations

Discourse Coherence

Discourse Relations

Informational Intentional


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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.


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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.


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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. SATELLITE (b) He's sure to veto House Bill 1711. 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

Computational Models of Discourse and Dialogue 2011: Conversation in Social Media

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