Natural Language and Dialogue Systems Lab Computational Models of Discourse and Dialogue 2011: Conversation in Social Media

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Persuasion in Social Media  Persuasion and argumentation in social media websites and forums

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ NLDS Social Media Dialogue Data  Data collected in the last year in collaboration with FoxTree’s Lab & Anand’s SemLab  Convinceme.net Convinceme  4forums.org 4forums  Carm.org Carm

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Using Mechanical Turk to get labels  p?pageId=1597&assignmentId=ASSIGNMENT_ID_N OT_AVAILABLE&hitId=1HNBWKACQBSEV0YDIO YSBWM1C0YNIP p?pageId=1597&assignmentId=ASSIGNMENT_ID_N OT_AVAILABLE&hitId=1HNBWKACQBSEV0YDIO YSBWM1C0YNIP  ageId=1398&assignmentId=ASSIGNMENT_ID_NOT _AVAILABLE&hitId=1CEJFP6T9BRSEF7QNPYEV9U3 7T7Y6W ageId=1398&assignmentId=ASSIGNMENT_ID_NOT _AVAILABLE&hitId=1CEJFP6T9BRSEF7QNPYEV9U3 7T7Y6W

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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Dialogue Structure  What makes a text coherent?  What are discourse structures?  Theories of discourse structures  Approaches to build discourse structures

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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  Hearers try to find connections between utterances in a discourse.  The possible connections between utterances can be specified as a set of coherence relations.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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. ……

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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 ] Parallel

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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 ] Parallel ] Explanation

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Discourse parsing Explanation (e1) S1 (e1) Parallel (e2;e4) Explanation (e2) S4 (e4) S2(e2)S3(e3)

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Why compute discourse structure?  Natural language understanding  Summarization  Information retrieval  Natural language Generation  Reference resolution

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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. Relation definitions and more.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Multinuclear relations  Contrast  Joint  List  Multinuclear restatement  Sequence

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Discourse structure John likes coffee He drinks it every day Mary hates coffee. They argue a lot elaboration contrast cause

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ A relation: Evidence  (a) George Bush supports big business.  (b) He’s sure to veto House Bill  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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ A relation: Volitional-Cause  (a) George Bush supports big business.  (b) He’s sure to veto House Bill  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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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) (b) (c) (b) (c) motivation condition

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

Natural Language and Dialogue Systems Lab Grosz & Sidner (1986)

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Grosz and Sidner (1986)  Three components:  Linguistic structure  Intentional structure  Attentional state

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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?

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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 15 th. 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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 DS3DS4 DS5 A1-C2A2-C3 A3C4-C7 C1

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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?

Natural Language and Dialogue Systems Lab Walker 1996: Limited Attention & Discourse Structure

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ How is attention modeled ?  Linear Recency  Hierarchical Recency

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ What about Processing & Centering?

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Informationally Redundant Utterances

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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).

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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],

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Cache Model (Human Working Memory)

Natural Language and Dialogue Systems Lab Building discourse structure

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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, ….

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Cues (cont)  Linguistic structure  Elaboration: for example, …  Concession: although  Condition: if  Sequence: and, first, second.  Contrast: and, … ……

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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, …

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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.

Natural Language and Dialogue Systems Lab Discourse Representation Theory

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Informational Components.  Data  Participants  Beliefs  Common ground  Intentions

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Formal Representations  Formal representation of informational components  Typed feature structures  Lists  Sets  Propositions  First order logic

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Dialog Moves  Trigger the update of the information state  Grammatical triggers  External events

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Update Rules  Govern information state updates  Sometimes incorporates domain knowledge  Sometimes govern behavior of dialog moves

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Control Strategy  Decide which update rule applies  Simple priority list  Game theory  Utility theory  Statistical methods

Natural Language and Dialogue Systems Lab Also for Dialogue Systems…

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Dialog Theories  Finite State Dialog Models  Plan-based Models

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 63 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 InformationalIntentional

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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].

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 65 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 66 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 67 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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 68 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Moore & Pollack 1992  Example 1  (a) George Bush supports big business. SATELLITE  (b) He's sure to veto House Bill 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

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ Moore & Pollack 1992  Example 1  (a) George Bush supports big business.  (b) He's sure to veto House Bill  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.

NATURAL LANGUAGE AND DIALOGUE SYSTEMS LABUC SANTA CRUZ 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