cpsc 503 computational linguistics

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CPSC 503Computational Linguistics

Discourse and DialogLecture 14

Giuseppe Carenini

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Finish form (22/10)

• Word Sense Disambiguation• Word Similarity• Semantic Role Labeling

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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

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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]

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Knowledge-Formalisms Map(including probabilistic formalisms)

Logical formalisms

(First-Order Logics)

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 ?

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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

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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



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Today 27/10



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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)• State of the art:

– 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?

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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.

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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

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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

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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]

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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)

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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

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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

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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

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Today 27/10



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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

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Dialog: two key tasks

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

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

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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

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Automatic Interpretation of Dialog Acts

Logical formalisms (First-Order Logics)

Morphology

Syntax

PragmaticsDiscourse

and Dialogue

Semantics

AI planners


versions)


versions)

Plan-Inferential

Cue-based

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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

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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

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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

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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

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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

OO Sequences of sequences

..can be computed with ……

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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

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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

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FSA Dialog Manager: system initiative

• xxx

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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)

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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.

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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

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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

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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

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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

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Next Time: Natural Language Generation

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

system that I developed and tested

cpsc 503 computational linguistics

Documents

generationcpsc503 winter

otherscpsc503 winter

linear positioncpsc503

stevenson 04cpsc503

semantic analysissyntax

mutual knowledge b

arg0 predicate

right nowii b