9/10/2015CPSC503 Winter 20091 CPSC 503 Computational Linguistics Discourse and Dialog Lecture 15...

04/21/23 CPSC503 Winter 2009 1

CPSC 503Computational Linguistics

Discourse and DialogLecture 15

Giuseppe Carenini

04/21/23 CPSC503 Winter 2009 2

Finish from (22/10)

• Word Sense Disambiguation (Thesaurus vs. Corpus)

• Word Similarity (Thesaurus vs. Distributional)

• Semantic Role Labeling

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WS: Distributional Methods

• Do not have any thesauri for target language

• If you have thesaurus, still– Missing domain-specific (e.g., technical words)– Poor hyponym knowledge (for V) and nothing for

Adj and Adv– Difficult to compare senses from different

hierarchies• Solution: extract similarity from corpora

• Basic idea: two words are similar if they appear in similar contexts

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WS Distributional Methods (1)

• Context: feature vector

Example: fi how many times wi appeared in the neighborhood of w

Stop list

),...,,( 21 Nfffw

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• More informative values (referred to as weights or measure of association in the literature)

• Point-wise Mutual Information

)()(

),(log),( 2

i

iiPMI wPwP

wwPwwassoc

• t-test

)()(

)()(),(),(

i

iiitestt

wPwP

wPwPwwPwwassoct

04/21/23 CPSC503 Winter 2009 6

WS Distributional Methods (3)• Similarity between vectors

Not sensitive to extreme values

)cos(),(cos

wv

wv

w

w

v

vwvsim ine

v

w

N

iii

N

iii

Jaccard

wv

wvwvsim

1

1

),max(

),min(),(

Normalized

(weighted) number of overlapping features

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• Best combination overall (Curan 2003)– t-test for weights– Jaccard (or Dice) for vector similarity

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Semantic Role Labeling: Example

– In 1979 , singer Nancy Wilson HIRED him to open her nightclub act .

– Castro has swallowed his doubts and HIRED Valenzuela as a cook in his small restaurant .

Employer Employee Task PositionSome roles..

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Supervised Semantic Role LabelingTypically framed as a classification problem

[Gildea, Jurfsky 2002]

• Train a classifier that for each predicate: – determine for each synt. constituent which semantic

role (if any) it plays with respect to the predicate

• Train on a corpus annotated with relevant constituent features

These include: predicate, phrase type, head word and its POS, path, voice, linear position…… and many others

04/21/23 CPSC503 Winter 2009 10

Semantic Role Labeling: Example

[issued, NP, Examiner, NNP, NPSVPVBD, active, before, …..]ARG0

predicate, phrase type, head word and its POS, path, voice, linear position……

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Supervised Semantic Role Labeling (basic) Algorithm

1. Assign parse tree to input

2. Find all predicate-bearing words (PropBank, FrameNet)

3. For each predicate.: apply classifier to each synt. constituent

Unsupervised Semantic Role Labeling: bootstrapping [Swier,

Stevenson ‘04]

04/21/23 CPSC503 Winter 2009 12

Knowledge-Formalisms Map(including probabilistic formalisms)

• Logical formalisms (First-Order Logics)• Thesaurus & corpus

based methods

Rule systems (and prob.

versions)

State Machines (and prob.

versions)

Morphology

Syntax

PragmaticsDiscourse

and Dialogue

Semantics

AI planners (MDPs Markov Decision

Processes)

Understanding

Generation

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Today 27/10

•Brief Intro Pragmatics•Discourse

– Monologue– Dialog

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“Semantic” Analysis

Syntax-driven and Lexical

Semantic Analysis

Sentence

Literal Meanin

g

Discourse

Structure

Meanings of

words

Meanings of grammatical structures

Context

Common-SenseDomain

knowledge

Intended meaning

FurtherAnalysis

INFERENCE

Pragmatics

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Pragmatics: Example

(i) A: So can you please come over here again right now

(ii) B: Well, I have to go to Edinburgh today sir(iii) A: Hmm. How about this Thursday?

What information can we infer about the context in which this (short and insignificant) exchange occurred ?

04/21/23 CPSC503 Winter 2009 16

Pragmatics: Conversational Structure



Not the end of a conversation (nor the beginning)

Pragmatic knowledge: Strong expectations about the structure of conversations

• Pairs e.g., request <-> response• Closing/Opening forms

04/21/23 CPSC503 Winter 2009 17

Pragmatics: Dialog Acts

• A is requesting B to come at time of speaking,

• B implies he can’t (or would rather not) • A repeats the request for some other time.Pragmatic assumptions relying on:

• mutual knowledge (B knows that A knows that…)

• co-operation (must be a response… triggers inference)

• topical coherence (who should do what on Thur?)



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Pragmatics: Specific Act (Request)

• A wants B to come over• A believes it is possible for B to come over• A believes B is not already there• A believes he is not in a position to order B

to…

Assumption: A behaving rationally and sincerely



Pragmatic knowledge: speaker beliefs and intentions underlying the act of requesting

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Pragmatics: Deixis

• A assumes B knows where A is• Neither A nor B are in Edinburgh• The day in which the exchange is taking

place is not Thur., nor Wed. (or at least, so A believes)

Pragmatic knowledge: References to space and time wrt space and time of speaking



04/21/23 CPSC503 Winter 2009 20

Today 28/10



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Discourse: Monologue• Monologues as sequences of “sentences” have structure• Tasks: Text Segmentation and Rhetorical (discourse)

parsing and generation

• Key discourse phenomenon: referring expressions (what they denote may depend on previous discourse)

Task: Coreference resolution

(like sentences as sequences of words)

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Discourse/Text Segmentation(1)• Simple approach:

– linear (unable to identify hierarchical structure)

– Subtopics, passagesUNSUPERVISED• Key idea: lexical cohesion (vs.

coherence)“There is not water on the moon.

Andromeda is covered by the moon.”• Discourse segments tend to be lexically cohesive

• Cohesion score drops on segment boundaries

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Discourse/Text Segmentation(2)SUPERVISED• Binary classifier (SVM, decision tree,

…)• : make yes-no boundary decision

between any two sentencesfeatures• Cohesion features (e.g., word

overlap, word cosine)• Presence of (domain specific)

discourse markers– News “good evening, I am.., joining us

now is…”– Real estate ads: is previous word phone

number?

04/21/23 CPSC503 Winter 2009 24

Sample Monologues: Coherence

House-A is an interesting house. It has a convenient

location. Even though house-A is somewhat far from

the park, it is close to work and to a rapid

transportation stop.

It has a convenient location. It is close to work. Even

though house-A is somewhat far from the park, house-

A is an interesting house. It is close to a rapid

transportation stop.

04/21/23 CPSC503 Winter 2009 25

Corresponding Text Structure

House-A is an

interesting house.

It has a convenient

location.

Even though house-A is

somewhat far from the park

it is close to

work

it is close to a rapid

transportation stop

EVIDENCECORE

EVIDENCE-1CONCESSION-1CORE-1

decomposition ordering rhetorical relations

04/21/23 CPSC503 Winter 2009 26

Text Relations, Parsing and Generation

• Parsing: Given a monologue, determine its rhetorical structure [Marcu, ’00 and ‘02]

• Generation: Given a communicative goal e.g., [convince user to quit smoking] generate structure – Next class

• Rhetorical (coherence) Relations: – different proposals (typically 20-30

rels)– Elaboration, Contrast, Purpose…

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• I saw him• I passed the course• I’d like the red one• I disagree with what you just

said• That caused the invasion

ReferenceLanguage contains many references

to entities mentioned in previous sentences (i.e., in the discourse context/model)

Two tasks• Anaphora/pronominal

resolution• Co-reference resolution

04/21/23 CPSC503 Winter 2009 28

Reference ResolutionTerminology

Referring expression: NL expression used to perform reference

Referent: “entity” that is referred

Types of referring expressions:

• Indefinite NP (a, some, …)• Definite NP (the, … )• Pronouns (he, she, her,...)• Demonstratives (this,

that,..)• Names

• Inferrables• Generics

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Pronominal Resolution: Simple Algorithm

• Last object mentioned (correct gender/person)– John ate an apple. He was hungry.

• He refers to John (“apple” is not a “he”)

– Google is unstoppable. They have increased..

• Selectional restrictions– John ate an apple in the store.

It was delicious. [stores cannot be delicious]It was quiet. [apples cannot be quiet]

• Binding Theory constraints– Mary bought herself a new Ferrari– Mary bought her a new Ferrari

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• Some pronouns don’t refer to anything– It rained

• must check if verb has a dummy subject

Additional Complications

• Evaluate “last object” mentioned using parse tree, not literal text position– I went to the GAP which is opposite to BR.– It is a big store.

[GAP, not BP]

04/21/23 CPSC503 Winter 2009 31

FocusJohn is a good studentHe goes to all his tutorialsHe helped Sam with CS4001He wants to do a project for Prof. Gray

He refers to John (not

Sam)

04/21/23 CPSC503 Winter 2009 32

Supervised Pronominal Resolution

Corpus annotated with co-reference relations (all antecedents of each pronoun are marked)• What features ?

(U1) John saw a nice Ferrari in the parking lot

(U2) He showed it to Bob

(U3) He bought it

04/21/23 CPSC503 Winter 2009 33

Need World Knowledge– The police prohibited the fascists from

demonstrating because they feared violence.

vs– The police prohibited the fascists from

demonstrating because they advocated violence. Exactly the same

syntax! • Not possible to resolve they without

detailed representation of world knowledge about feared violence vs. advocated violence

04/21/23 CPSC503 Winter 2009 34

Coreference resolution• Decide whether any pair of NPs co-

refer• Binary classifier again

NPj

• What features?Same as for anaphora + specific ones to

deal with definite and names. E.g.,– Edit distance– Alias (based on type – e.g., for PERSON:

Dr. or Chairman can be removed)– Appositive (“Mary, the new CEO, ….”

anaphorantecedents

04/21/23 CPSC503 Winter 2009 35

Knowledge-Formalisms Map(including probabilistic formalisms)

• Logical formalisms (First-Order Logics)• Thesaurus & corpus

based methods


versions)


versions)

Morphology

Syntax

PragmaticsDiscourse

and Dialogue

Semantics

AI planners (MDPs Markov Decision

Processes)

Understanding

Generation

04/21/23 CPSC503 Winter 2009 36

Next Time: Natural Language Generation

• Read handout on NLG• Lecture will be about an NLG

system that I developed and tested

04/21/23 CPSC503 Winter 2009 37

Today 28/10



04/21/23 CPSC503 Winter 2009 38

Discourse: Dialog• Most fundamental form of language use• First kind we learn as children

Dialog can be seen as a sequence of communicative actions of different kinds (dialog acts) - (DAMSL 1997; ~20) Example:

(i) A: So can you please come over here again

right now(ii) B: Well, I have to go to Edinburgh today sir(iii) A: Hmm. How about this Thursday(vi) B: OK

ACTION-DIRECTIVE

REJECT-PART

ACCEPTACTION- DIRECTIVE

04/21/23 CPSC503 Winter 2009 39

Dialog: two key tasks

• (1) Dialog act interpretation: identify the user dialog act

• (2) Dialog management: (1) & decide what to say and when

04/21/23 CPSC503 Winter 2009 40

Dialog Act Interpretation

• What dialog act a given utterance is?

E.g., I’m having problems with the homework

• Surface form is not sufficient!

– Statement - prof. should make a note of this, perhaps make homework easier next year

– Directive - prof. should help student with the homework

– Information request - prof should give student the solution

04/21/23 CPSC503 Winter 2009 41

Automatic Interpretation of Dialog Acts

Logical formalisms (First-Order Logics)

Morphology

Syntax

PragmaticsDiscourse

and Dialogue

Semantics

AI planners


versions)


versions)

Plan-Inferential

Cue-based

04/21/23 CPSC503 Winter 2009 42

Cue-Based: Key Idea

Words and collocations: • Please and would you -> REQUEST• are you and is it -> YES-NO-QUESTIONs

Conversational structure: • Yeah following PROPOSAL ->

AGREEMENT• Yeah following INFORM ->

BACKCHANNEL

Prosody: Loudness or stress yeah -> AGREEMENT vs. BACKCHANNEL

04/21/23 CPSC503 Winter 2009 43

Cue-Based model (1)Each dialog act type (d) has its own micro-grammar

which can be captured by N-gram models

Lexical: given an utterance W= w1 … wn for each dialog act (d) we can compute P(W|d)

Prosodic: given an utterance F= f1 … fn for each dialog act (d) we can compute P(F|d)

AnnotatedCorpus

Corpus for d1……

Corpus for dm

……

Split N-gram models1

N-gram modelsm

04/21/23 CPSC503 Winter 2009 44

Cue-Based model (2)Conversational structure: Markov chain

AnnotatedCorpus

d1

d2

d3

d4

d5

.8.3

.7 .5

1

1 .2.3

1

.2

Combine all info sources: HMM

di-1 di

Fi , WiFi , Wi

)|( 1ii ddP

)|,( iii dFWP)|()|(

)|,(

iiii

iii

dFPdWP

dFWP

N-gram models!

…

Fi , Wi

04/21/23 CPSC503 Winter 2009 45

Cue-Based model Summary• Start form annotated corpus (each

utterance labeled with appropriate dialog act)

• For each dialog act type (e.g., REQUEST), build lexical and phonological N-grams • Build Markov chain for dialog acts (to express conversational structure)

• Combine Markov Chain and N-grams into an HMM• Now ),|(maxarg FWDP

D

PP Sequences of sequences

..can be computed with ……

04/21/23 CPSC503 Winter 2009 46

Dialog Managers in Conversational Agents

• Examples: Airline travel info system, restaurant/movie guide, email access by phone

• Tasks– Control flow of dialogue (turn-

taking)– What to say/ask and when

04/21/23 CPSC503 Winter 2009 47

Dialog Managers

Logical formalisms (First-Order Logics)

Morphology

Syntax

PragmaticsDiscourse

and Dialogue

Semantics

AI planners(and prob.

versions)


versions)


versions)FSA

Template-Based

BDIMDP

27/10: Probably stop here

04/21/23 CPSC503 Winter 2009 48

04/21/23 CPSC503 Winter 2009 49

Plan Inferential (BDI) Pros/Cons

• Powerful: uses rich and sound knowledge structures -> should enable modeling of subtle indirect uses of dialog acts

• Time-consuming:– To develop– To execute

• Ties discourse processing with non-linguistic reasoning -> AI complete

• Dialog acts are expressed as plan operators involving belief, desire, intentions

04/21/23 CPSC503 Winter 2009 50

FSA Dialog Manager: system initiative

• xxx

04/21/23 CPSC503 Winter 2009 51

Template-based Dialog Manager (1)

• GOAL: to allow more complex sentences that provide more than one info item at a time

S: How may I help you?U: I want to go from Boston to Baltimore on the 8th. Slot Optional questions

From_Airport “From what city are you leaving?”To_Airport “Where are you going?”Dept-Time “When do you want to leave?”Dept-Day …………… ………… • Interpretation: Semantic Grammars,

semi-HMM, Hidden-Understanding-Models (HUM)

04/21/23 CPSC503 Winter 2009 52

Template-based Dialog Manager (2)

• More than one template: e.g., car or hotel reservation

• User may provide information to fill slots in different templates

• A set of production rules fill slots depending on input and determines what questions should be asked next

E.g., IF user mention car slot and “most” of

air slot are filled THEN ask about remaining car slots.

04/21/23 CPSC503 Winter 2009 53

Markov Decision Processes [’02]

• Common formalism in AI to model an agent interacting with its environment.

• States / Actions / Rewards• Application to dialog:– States: slot in frame currently worked

on, ASR confidence value, number of questions about slot,..

– Actions: questions types, confirmation types

– Rewards: user feedback, task completion rate

04/21/23 CPSC503 Winter 2009 54

BDI Dialog Manager

Sys to understand U2 needs model of preconditions, effects, decomposition of:– meeting event (precon: be “there”)- fly-to plan (decomp: book-flight, take-flight)- Take-flight plan (effect: be “there”)

S1: How may I help you?

U1: I want to go to Pittsburgh in April.

S2: And, what date in April do you want to travel?

U2: Uh hmm I have a mtg. there on the 12th.

REQUEST ACKNOWLEDGE

INFORMREQUEST

04/21/23 CPSC503 Winter 2009 55

BDI Dialog Manager

Sys to generate S2 needs model preconditions of:- Book-flight action (agent knows departure date and

time)

S1: How may I help you?U1: I want to go to Pittsburgh in April.

S2: And, what date in April do you want to travel?

U2: Uh hmm I have a mtg. there on the 12th.

REQUEST ACKNOWLEDGE

INFORMREQUEST

Integrated with logic-based planning system

• Understanding an utterance: plan recognition (recognize multiple goals)

• Generating an utterance: plan generation (possibly) satisfying multiple goals

04/21/23 CPSC503 Winter 2009 56

Designing Dialog Systems: User-Centered Design

• Early Focus on User and Task: e.g., interview the users

• Build Prototypes: Wizard-of-Oz (WOZ) studies

Iterative Design

• Evaluation

04/21/23 CPSC503 Winter 2009 57

Next Time: Natural Language Generation

• Read handout on NLG• Lecture will be about an NLG

system that I developed and tested

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9/10/2015CPSC503 Winter 20091 CPSC 503 Computational Linguistics Discourse and Dialog Lecture 15...

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