Discourse
Foundations of Speech and
Language Technology:
= a unit of language (language use) consisting of
more than one utterance
– Utterance = the use of a NL expression (sentence, …)
speaker S, to hearer(s) H, at time t, in situation s
Discourse and Dialogue
• Monologue vs. dialogue
• Written vs. spoken, or multimodal
Ivana Kruijff-Korbayová
[email protected]
• Characteristics:
www.coli.uni-sb.de/~korbay/ ! Teaching
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– Purpose; Collaboration; Coherence; Cohesion
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Purpose
Collaboration
• Collaboration:
• Purpose:
– There is a reason why S and H interact:
they have some goal(s) they want to achieve
– There is a reason why any part of the
discourse is there:
it contributes to achieving some goal(s)
• S and H may have joint (shared) goals or
individual (different) goals
– Communication is inherently a collaborative activity:
S and H work together to establish and achieve their
goals
• Cooperative Principle (Grice)
– Make your contribution such as is required, at the
stage at which it occurs, by the accepted purpose or
direction of the talk exchange in which you are
engaged
• Maxims of Conversation
–
–
–
–
– Cooking dinner together vs.
Getting someone to come to a surprise party
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Maxim of quality
Maxim of quantity
Maxim of relevance
Maxim of manner
Ivana Kruijff-Korbayová: Discourse&Dialogue
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Cf. [1]: Chapter 19
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Cohesion
Coherence
• Cohesive devices: linguistic means that make a
discourse stick together
– anaphoric expressions, discourse connectives,
lexical chains …
Two guys were working for the city. One would
furiously dig a hole, then the other would come
behind him and quickly fill the hole. They were
drenched in sweat.
Two guys were working for the city. One would
furiously dig a hole, then the other would come
behind him and quickly fill the hole. They were
drenched in sweat.
Two guys were working for the city. He likes cake.
A townhall is near a river.
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= Making sense together, as a whole:
the parts contribute in a meaningful way
5
Two guys were working for the city. He likes cake.
A townhall is near a river.
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Outline
•
•
•
•
•
Anaphoric reference
Discourse relations
Discourse structure
Speech acts
Grounding
Anaphoric Reference
Basic reading:
[1]: Chapter 18, Section 18.1
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Reference
Discourse Model
• Universe of discourse entities introduced as
“referents” of linguistic expressions
Objects in reality
– Operations:
• Evoke (new) discourse entity
• Access (old) discourse entity
Entities in a discourse model
– Discourse entity status:
• New, old, inferable
– Basic types of discourse entities:
• Objects (concrete/abstract) vs. events (states)
• Basic types of referring expressions:
– Noun phrases, pronouns
– Temporal and spatial expressions
Referring expressions
A guy just finished running.
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Anaphoric Reference
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Anaphoric Reference
Objects in reality
• Coreference
– Anaphoric expression refers to the same
entity as its antecedent
(identity of referent)
Entities in discourse model
• Bridging
evoke
access
evoke
A guy just finished running. He is tired. The sweat is dripping from his body.
Antecedent
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Anaphoric expressions
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– Anaphoric expression refers to a different
entity than the antecedent
there is an association relationship between
the referents, e.g., part-whole, set-member,
entity-attribute… (the entity is inferable)
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Exercise
Anaphora Resolution
Two guys were working for the city. One would furiously dig
a hole, then the other would come behind him and quickly
fill the hole. They were drenched in sweat.
A man watching from the sidewalk couldn't believe how hard
they were working, but also couldn't understand what they
were doing. Finally he said:
"I'm confused. You dig a hole and then your partner comes
behind you and fills it up again!"
The digger leaned on his shovel and replied,
"Oh yeah, it must look funny. You see, the lazy jackass who
plants the trees is sick again today!"
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• Anaphora is a device of language economy,
it’s natural and mostly easy for humans
• Why is it a problem for NLP?
–
–
–
–
Information extraction (topic segmentation)
Summarization
Machine translation
Dialogue systems:
(1) U: Do any samples contain magnesium?
S: Yes. R560 and R668.
U: And do they contain ruthenium?
(2) S: Do any samples contain magnesium?
U: No.
S: And do they contain ruthenium?
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Anaphora Resolution
• Criteria on antecedent/anaphor pairs:
– Determine referents
(= for each referring expression, determine
how the discourse model is to be updated)
• Task of anaphora resolution:
– Identify anaphors
– Identify antecedents
– Identify relationships
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Anaphora Resolution
• Task of reference resolution:
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–
–
–
–
–
Agreement (person, gender, number)
Syntactic relationships (binding)
Lexical repetition (edit distance)
Selectional restrictions on arguments
Salience: recency, grammatical role,
semantic orientation, etc.
– Repeated mention count
– Parallelism
– World knowledge
Cf. [1]: Chapter 18, pp. 678-684
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Coreference Resolution
Algorithms
Anaphora Resolution
• Search:
• Simplest: recency-based:
– Given an anaphor, systematically consider one
potential antecedent after another
• Best-first (in structured search-space)
• Hard to optimize multiple decisions
• Classification:
– Given all potential anaphor-antecedent pairs,
decide yes/no (and optionally assign score)
• Compute&evaluate all pairs
• Local and global optimization: machine learning
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– Look back & rule out incompatible candidates
• There are various other approaches:
–
–
–
–
–
Hobb’s 1978: syntactic search
Brennan et al. 1987: Centering-based
Lappin&Leass 1994: weighted salience factors
Baldwin 1995: specialized high precision rules
Recent machine learning methods
Cf. [1]: Chapter 18: pp. 684-694
but do not need to know for exam
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Exercise
Two guys were working for the city. One would furiously dig
a hole, then the other would come behind him and quickly
fill the hole. They were drenched in sweat.
A man watching from the sidewalk couldn't believe how hard
they were working, but also couldn't understand what they
were doing. Finally he said:
"I'm confused. You dig a hole and then your partner comes
behind you and fills it up again!"
The digger leaned on his shovel and replied,
"Oh yeah, it must look funny. You see, the lazy jackass who
plants the trees is sick again today!"
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Discourse Relations
(coherence/ rhetorical relations)
Basic reading:
[1] Chapter 18, Section 18.2, 18.3;
[1] Chapter 19, Section 19.4
[2]
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Discourse Relations
Discourse Relations
• Various specific types of connections
relating utterances in discourse
• These connections add meaning beyond
the propositional content of each of the
segments alone
• Sometimes they are explicitly signaled by
discourse connectives and other markers
1. Two guys were working for the city.
Elaboration
2. One would furiously dig a hole,
Occasion
3. then the other would come behind him
Occasion
4. and quickly fill the hole.
Result
5. They were drenched in sweat.
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Discourse Relations
Two guys were working for the city. One would furiously dig
a hole, then the other would come behind him and quickly
fill the hole. (As a result) They were drenched in sweat.
A man watching from the sidewalk couldn't believe how hard
they were working, but also couldn't understand what they
were doing.
Finally he said: "I'm confused. You dig a hole and then your
partner comes behind you and fills it up again!"
The digger leaned on his shovel and replied,
"Oh yeah, it must look funny. You see, the lazy jackass who
plants the trees is sick again today!"
– Peter eats vegetables. He is healthy.
• Explanation(b,a): eb causes ea
– Peter is healthy. He eats vegetables.
• Elaboration(b,a): eb included in ea
– Peter went to the mountains. He skied every day.
• Occasion: ea before eb
– Peter bought skis. He went to the mountains.
• Parallel(a,b): ea and eb are similar
– Peter eats vegetables. Paul regularly sports.
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Exercise
• Result(b,a): ea causes eb
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Discourse Relation Resolution
Discourse Relation Recognition
• Knowledge-intensive (suitable in limited domains):
• Why?
– Information extraction (topic segmentation)
– Summarization
– Dialogue systems:
e.g., revision vs. occasion
U: OK. That’s good. Now I’d like you to find and
show the song Jingle Bells. Please search for the
song Jingle Bells.
– Inference-based:
• Encode discourse relations as axioms
• Construct a proof for discourse (abduction)
– Plan-based
• Encode discourse relations as plan operators
• Instantiate plan for discourse goal
• Knowledge-poor (suitable on large scale):
– Discourse grammar-based (brittle)
• Encode discourse relations as structural rules
• Parse discourse
– Shallow use of various surface clues (robust)
• Supervised machine learning (needs annotated data)
(cf. work by Daniel Marcu or Simone Teufel)
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Discourse Structure
1. Two guys were working for the city.
2. One would furiously dig a hole,
3. then the other would come behind him
4. and quickly fill the hole.
5. They were drenched in sweat.
– Subordination (embedding)
e.g., elaboration, result, explanation
– Coordination (linear precedence)
e.g., parallel, occasion
Elaboration
Result
• Recursively built discourse segments
• Each segment has a purpose
Occasion
1
Cf. [1]: 18.3, 19.4
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Discourse Structure
• Discourse is not just a flat linear
sequence of utterances, but has
hierarchical structure
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2
3
4
5
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Three Layers
of Discourse Structure
Discourse Structure
• Discourse segment recognition
Grosz and Sidner 1986:
• Linguistic structure
– Discourse markers (cue phrases), e.g., now, well, so
– Prosodic indicators:
– Segments marked by linguistic means
• Change of pitch range
– decreasing within segment, reset at boundary
• Intentional structure
• Speed
– Faster for embedded segment
– Hierarchically related discourse purposes
• Pauses at boundaries
• Attentional structure
– Tense and mood changes
– Topic changes tend to correlate with segment boundaries
– Use of anaphoric expressions
– Stack of “focus spaces” (accessible entities)
• Antecedents accessible within segment
• Antecedent inaccessible across segment boundaries
Cf. [2]: Section 11.3
but need not know for exam
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Cf. [2]: p. 442
but need not know for exam
The levels are mutually co-constraining.
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Global Discourse Structure
• Particular discourse genres typically
exhibit regular structural patterns
– Scientific paper: abstract, introduction, body
sections, related work, conclusions
– Story: introduction, climax, ending
– Recipe: ingredients, procedural steps, serving
suggestions
– News article: summary, detailed story
– Telephone call: greetings, body section(s),
closing
–…
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Speech Acts
(Dialogue Acts/Moves)
Basic Reading:
[1] Chapter 19
(need know know details of 19.3 and 19.5)
[3] (need not know details)
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Speech Acts
Speech Act Types
Speech act theory [Austin, Searle]
Assertive
S commits to sth
being the case
Comment, suggest,
swear, boast, conclude
Directive
S attempts to get H
do sth
Ask, order, request,
beg, invite, advise
Commissive
S commits to future
course of action
Promise, plan, vow,
bet, oppose
Expressive
S expresses
psychological state
Thank, apologize,
welcome, deplore
Declarations
S changes world
Resign, name, fire
– how to do things with words
• Utterances bring about acts on context
– Locutionary act: the act of uttering the words with
their semantic content
– Illocutionary act: the communicative act the speaker
intends to perform by saying the words = speech act
– Perlocutionary act: the act that occurs as a
result/effect of the utterance, intended or
unintended (e.g., making someone laugh, scared…)
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Conversation Structure
• Why?
– Expected SAs
– To determine what user wants
– And to decide about an appropriate response,
e.g., accept/reject statement vs. fulfill/turn-down request
• E.g., Opening, body, closing of telephone call(s)
• Some SA sequences occur regularly, are even
conventionalized
(obligation to respond, preferred responses)
• How do we decide what DA a user input is, e.g.,
statement vs. info-request
Greeting-greeting
Question-answer
Compliment-downplayer
Accusation-denial
Offer-acceptance
Request-grant
…
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Speech Act Recognition
• Common overall organization
–
–
–
–
–
–
–
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– At first glance, this looks simple: different syntax:
• Yes-no-questions have subj-verb inversion
• Statements have declarative syntax
• Commands have imperative syntax
• However, the mapping between surface form and
illocutionary act is not one-to-one
38
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Speech Act Recognition
Speech Act Recognition
• For example, what looks like a yes/no question
• Another example of “indirectness”:
Can you give me a list of the flights from A to B
A: That’s the telephone.
B: I’m in the bath.
A: OK.
Can be a polite form of directive or request
Please give me a list of flights from A to B
• What looks like a statement
And you said you wanted to travel next week
• Can be paraphrased as follows:
Can actually be a question, used to verify sth.
(intonation?)
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A requests B to perform action (answer phone)
B states reason why he cannot comply (in bath)
A undertakes to perform action (answer phone)
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Speech Act Recognition
41
• Plan-based interpretation
– Literal meaning (direct speech act)
– Idiomatic meaning (indirect speech act)
the grammar lists idiomatic meanings for
each construction, e.g., “Can you X?”
has request as one possible meaning
• Inferential model: indirect speech acts
arrived at by inference
Ivana Kruijff-Korbayová: Discourse&Dialogue
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Ivana Kruijff-Korbayová: Discourse&Dialogue
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Automatic SA Recognition
• Idiom-based model:
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– Essentially the inference model, differences lie in
amount and depth of actual reasoning
– Symbolic
– Requires hand-coding and domain-knowledge
• Cue-based recognition
– Essentially derived from the idiom model
– Using a combination of utterance features and
context features (supervised machine learning
methods)
– Requires hand-annotated data
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Example/Exercise
Grounding
Basic reading:
[1] Chapter 19
[4]
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Grounding
Intention
S proposes project w
H considers project w
Proposition
S signals that p
H recognizes that p
Signal
S presents signal s
H identifies signal s
Channel
S executes behavior t
H attends to behavior t
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Positive Grounding Feedback
• Establishing common ground
• Levels of interpretation (Clark 1996):
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• Continued attention
• Relevant next contribution
• Acknowledgement (nod or “continuer”, e.g., uhhuh, yeah; or assessment, e.g., that’s great)
• Demonstration (by paraphrasing,
reformulating or cooperatively completing)
• Display (verbatim repetition)
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weaker
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Positive Grounding Feedback
A: I'm confused. You dig a hole and then
your partner comes behind you and fills it
up again!
B: Oh yeah, it must look funny. You see, the
lazy jackass who plants the trees is sick
again today!
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Grounding Problems
• Grounding problems are due to
–
–
–
–
Lack of perception or understanding
Ambiguity
Conflicts (differences in beliefs)
Misunderstanding (misinterpretation)
• Clarification and repair strategies, e.g.,
ask for clarification, repetition, rephrase
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Modeling Grounding Acts
Grounding Acts
• Traum (1999): grounding acts
• Traum (1999)
Cont(I, Uk ,DUi ) | SelfRepair(I, Uk, DUi)
DUi:
S
Init(I, Uk ,DUi )
1
Ack(R, Uk ,DUi )
Cancel(I, Uk ,DUi )
Cont(I, Uk ,DUi ) | SelfRepair(I, Uk, DUi)
DUi:
F
S
Cancel(I, Uk ,DUi )
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Init(I, Uk ,DUi )
1
Ack(R, Uk ,DUi )
Cancel(I, Uk ,DUi )
D
(1) 1:A: Move the boxcar to Corning Init(A,1,DU1)
2:A: and load it with oranges
Cont(A,2,DU1)
3:B: OK
Ack(B,3,DU1)
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50
F
Cancel(I, Uk ,DUi )
D
(1) 1:A: Move the boxcar to Corning Init(A,1,DU1)
2:: and load it with oranges
Cont(A,2,DU1)
3:B: OK
Ack(B,3,DU1)
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DUi:
S
Grounding Acts
Grounding Acts
Cont(I, Uk ,DUi ) | SelfRepair(I, Uk, DUi)
Cont(I, Uk ,DUi ) | SelfRepair(I, Uk, DUi)
Init(I, Uk ,DUi )
1
Ack(R, Uk ,DUi )
DUi:
F
S
[REPAIR(R, Uk ,DUi )]
[REQREPAIR(R, Uk, DUi)]
Cancel(I, Uk ,DUi )
(5) 1:A: Move the boxcar to Bath
2:B: Bath?
3:A: Oh, Corning.
4:B: OK
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Init(I, Uk ,DUi )
1
Ack(R, Uk ,DUi )
F
[REPAIR(R, Uk ,DUi )]
[REQREPAIR(R, Uk, DUi)]
D
Cancel(I, Uk ,DUi )
Cancel(I, Uk ,DUi )
Init(A,1,DU1)
ReqRepr(B,2,DU1)
! Init(B,2,DU2)
Ack(A,3,DU2)
! Repair(A,3,DU1)
Ack (R,4,DU1)
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D
(4) 1:A: Move the boxcar to Bath
2:B: To Corning
3:A: Oh, sure.
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Example/Exercise
Cancel(I, Uk ,DUi )
Init(A,1,DU1)
Repair(B,2,DU1)
! Init(B,2,DU2)
Ack (A,3,DU2)
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Wrap-Up
• Language use is rife with challenging discourse level
phenomena:
–
–
–
–
Anaphoric reference
Discourse relations
Speech acts
Grounding acts
• Co-constraining aspects: structure, attention, intention
• Interpretation
– Ultimately requires inference and world knowledge
• Possible in limited domains
– Can be approximated using surface clues (robust, large scale)
• Generation (see [1]: Chapter 20, Section 20.4)
– Naturalness, economy --> easy to understand for users
– Approximation according to available resources/information
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Basic Reading
[1] D. Jurafsky and J. Martin (2000):
Speech and Language Processing
Chapters 18, 19, 20. pp. 669-798.
[2] B. Grosz, M. Pollack and C. Sidner (1989): Discourse. In
Foundations of Cognitive Science. M. Posner (ed.). MIT
Press. pp. 437-468.
[3] D. Jurafsky (2006): Pragmatics and Computational
Linguistics. In: Handbook of Pragmatics, L.R. Horn and
G. Ward (eds.). Oxfrod: Blackwell.
http://www.stanford.edu/~jurafsky/prag.pdf (draft v.)
[4] D. Traum (1999): Computational Models of Grounding in
Collaborative Systems. AAAI Fall Symposium on
Psychological Models of Communication. Pp. 124-131.
http://people.ict.usc.edu/~traum/Papers/psych.ps
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