discourse dialogue

7/26/2019 Discourse Dialogue

1/98

Dialogue and Discourse

analysis

(some slides borrowed from

D. Jurafski and from S.Ponzetto)


2/98

Natural Language Processing:leels of re!resentation

Morphology

Synta

x

Semantics

Pragmatics

Words"#

"

$

%&

D 'D


3/98

Meaning

in context

Processing flow of D&D Processing

Sound

waves&S#

WordsSyntacti

c!rocessi

ng

ParsesSemanti

c!rocessi

ng

MeaningDiscourseDialog

ue!rocessi

ng


4/98

Discoursedialogue analysis

So far we always analyzed onesentence in isolation syntacticallyandor semantically

Natural languages are s!oken orwritten as a collection of sentences

"n general a sentence or utterancecannot be understood in isolation.


5/98

& dialog e*am!le+u and #udnicky (,---)


6/98

A discourse example

Jon went to te bank to de!osit is!ayceck.

/e ten took a train to 0ill1s cardealersi!.

/e needed to buy a car.

2e com!any e works for now isn1tnear any !ublic trans!ortation.

/e also wanted to talk to 0ill aboutteir softball league.


7/98

"ssues in discoursedialogue

Dialogue2urn3taking

S!eec act

4rounding Dialogue management

Discourse Segmentation coerence relations

0ot &na!ora

5o3reference


8/98

6. 2urn3taking

Dialogue is caracterized byturn3taking.&:0:&:0:

7 #esource allocation !roblem: /ow do s!eakers know wen to take te

8oor9


9/98

2urn3taking rulesSacks et al. (6;#N32&?"N4 #$%>"#$S 2@ D$A"P#@2@5@L


10/98


Dialogue2urn3taking

S!eec act



0ot &na!ora

5o3reference


11/98

,. S!eec &cts

S!eaker1s contribute more informationtan Bust Cwat is said

S!eec &cts can gie a !rinci!ledaccount of additional meaning

S!eec &ct 2eory can also el! use*amine utterances from te!ers!ectie of teir function ratertan teir form


12/98

5lassiEcation of S& according toCforce

Locutionary Force (what is said)

Bring the chair to the dining room

Illocutionary Force (what is done)

The robot is asked to grasp a chair andchange his current position

Perlocutionary Force (the efect)

The current position of the robot&chairchanges to dining room (if action issuccessfully performed)


13/98

2e F leels of act reisited

LocutionaryForce

IllocutionaryForce

PerlocutionaryForce

Can I have the restof your sandwich?OrAre you going tofinish that?

Interrogative Stop eating thesandwich

Effect: ou give !esandwich "or youare a!used #y !y$uoting fro!%&iner'( "or etc(

I want the rest ofyour sandwich

&eclarative Stop eating thesandwich

Effect: as a#ove

)ive !e yoursandwich*

&irective Stop eating thesandwich

Effect: as a#ove+


14/98

2y!es of S!eec acts (more Ene3grained)

Commissives (Afect Speaer! Su"#ective)

$%P&S' ath! fer! Promise

I promise you a new book

eclaratives (Change the *acrocosmic Social +orld)

$%P&S' ,aptism! *arriage I will spend my vacations in ardinia

irectives (Change the *icrocosmic Social +orld)

$%P&S' Command! -e.uest

!ove the chair near the table

&xpressives (Feelings o/ Speaer) $%P&S' Apology! $hans

orry" I did not understood correctly

(*ey 012! Searle 0344! 56)


15/98

2y!es of S!eec acts (more Ene3grained)

Interrogatives (7earer 8nows ,est)

$%P&S' Closed (yes9no .uestions! list)! pen (who:when:;;) #o you see the co$e maker somewhere in the kitchen%

Imperatives (irectives) (Afect 7earer)

$%P&S' -e.uest! -e.uirement! $hreat! +arning o to the dining room

Per/ormatives (Afect world)

$%P&S' Agreement! Appointment! ,aptism! eclarationo/ Independence! edication! *arriage

'ou are right

-epresentatives ("#ective escriptive Statements)

$%P&S' Statement that is either $rue or False

The co$ee maker is on the table


16/98

>se of s!eec acts in dialogue:s!eec3act frames

(Arost et al. ,-6-)

ce "ythe lo""y!?


17/98


Dialogue2urn3taking

S!eec act



0ot &na!ora

5o3reference


18/98

F. 4rounding

Gy do eleator buttons ligt u!9

5lark (6H) (after Norman 6II)

rinciple of closure. &gents!erforming an action reuireeidence suKcient for current!ur!oses tat tey aesucceeded in !erforming it

Gat is te Clinguistic correlate oftis9


19/98

4rounding

Need to know weter an actionsucceeded or failed

Dialogue is also an action a collective action !erformed by s!eaker and

earer

Common ground: set of tings mutuallybelieed by bot s!eaker and earer

Need to aciee common ground soearer must groundor acknowledges!eakers utterance.


20/98

/ow do s!eakers ground95lark and Scaefer

5ontinued attention: 0 continues attending to &

#eleant ne*t contribution: 0 starts in on ne*t releant contribution

&cknowledgement: 0 nods or says continuer like uh*huhyeah assessment

(great)

Demonstration: 0 demonstrates understanding & by !ara!rasing or

reformulating &1s contribution or by collaboratielycom!leting &1s utterance

Dis!lay: 0 dis!lays erbatim all or !art of &1s !resentation


21/98

& uman3umanconersation


22/98

4rounding e*am!les

Dis!lay: 5: " need to trael in ay

&: &nd wat day in aydid you want to

trael9

&cknowledgement 5: /e wants to 8y from 0oston

&: mm3mm 5: to 0altimore Gasington "nternational

Mm3mm (usually transcribed Cu3u) is

abackcannel continuer or


23/98

4rounding $*am!les (,)

&cknowledgement O ne*t releantcontribution &nd wat day in ay did you want to

trael9 &ndyou1re 8ying into wat city9

&ndwat time would you like to leae9

2e andindicates to te client tatagent as successfully understoodanswer to te last uestion.

4 di i


24/98

4rounding negatieres!onsesArom 5oen et al. (,--


25/98

4rounding and DialogueSystems 4rounding is not Bust a tidbit about

umans

Is ey to design o/conversational agent

Gy9 /5" researcers End users of s!eec3

based interfaces are confused wensystem doesn1t gie tem an e*!licitacknowledgement signal

Stifelman et al. (6F) =ankeloic et al.


26/98

$*am!le


27/98


Dialogue2urn3taking

S!eec act



0ot &na!ora

5o3reference


28/98

Dialogue management

& dialogue system is Enalized to some !ur!ose (e.g.a 8igt reseration)

>nlike for discourse analysis a structure must "e

determined a9priori to guide the conversation Dialogue manager: Cforces te dialogue between

user and system to follow one or more structures

Aor s!eec dialogue systems most common

a!!roaces are: Ainite state dialogue manager

Arame and slot semantics

&gent3based dialogue manager


29/98

Ainite3state dialoguemanagers

System com!letely controls teconersation wit te user.

"t asks te user a series of uestions

"gnoring (or misinter!reting)anyting te user says tat is not adirect answer to te system1s

uestions


30/98

Ainite State Dialogueanager


31/98

System forces te user to follow testructure


32/98

Ainite3state a!!roac

Pros sim!le to write

ery robust and uick

Cons System direct entire conersation

>ser actions ery limited


33/98

33

Frame-based Approach

Frame-ased s!stem As"s t#e user $uestions to fill slots in a templatein order to

perform a tas" %form-filling tas"

Permits t#e user to respond more flexil! to t#e s!stem's

prompts %see (xample ) *ecogni+es t#e main concepts in t#e user's utterance

Example 1)

System: What is your destination?

User: London.

System: What day do you want to

travel?

User: Friday

Example 2)

System: What is your destination?

User: London on Friday around

10 in the morning.

System: I have the following

onnetion !


34/98

ArameSlot semantics

Sow me morning 8igts from 0oston to SAon 2uesday.

S/@G:

AL"4/2S:@#"4"N:

5"2=: Boston

D&2$: Tuesday

2"$: morning

D$S2:

5"2=: San Francisco


35/98

Arameslot Semantics (multi!lesentences)

Slot %uestion "D$N2"A= Gat is your name9

@#"4"N Gat city are you leaing from9

D$S2 Gere are you going9

D$P2 D&2$ Gat day would you like to leae9

D$P2 2"$Gat time would you like to leae9

&"#L"N$ Gat is your !referred airline9

e te structure of te frame itself to guide dialogu


36/98

36

Advantages ,#e ailit! to use natural language multiple

slot filling

,#e s!stem processes t#e user's over-informative answers and corrections

DisadvantagesAppropriate for well-defined tas"s in w#ic# t#e

s!stem ta"es t#e initiative in t#e dialog Difficult to predict w#ic# rule is li"el! to fire in

a particular context

Arame3based a!!roaces


37/98

37

Properties .omplex communication using unrestricted natural language

"i#ed$Initiative

%o$operative pro&lem solving

,#eorem proving planning distriuted arc#itectures

.onversational agents

(xamplesUser:Im looking for a job in the Calais area. Are there any servers?

System:No, there arent any employment servers for Calais. However, there is

an employment server for Pase!Calais an an employment server for "ille.

Are yo# intereste in one of these?

Syste! atte!pts to provide a !ore co,operative response that !ight address the

user-s needs+

&gent3based a!!roaces


38/98

38

Agent-based Approach

Advantages

Suitale to more complex dialogues

Mixed-initiative dialogues

Disadvantages

Muc# more complex resources and

processing

Sop#isticated natural language capailities .omplicated communication etween

dialogue modules


39/98

5>3systems (@lym!us)


40/98


Dialogue2urn3taking

S!eec act

4rounding

Dialogue management

Discourse Segmentation

coerence relations

0ot &na!ora

5o3reference


41/98

Discourse segmentation

Separating a documentinto a linearseuence of subto!ics Aor e*am!le: scientiEc articles are segmented

into &bstract "ntroduction etods #esults5onclusions

Note: tis is a sim!liEcation Q a discoursemigt ae a more com!le* structure

&!!lications: Summarization: summarize eac segmentse!arately

"nformation #etrieal "nformation $*traction:

&!!ly to an a!!ro!riate i.e. relevantsegment


42/98

Discourse segmentation

$*am!le: ,6 !aragra! article calledtarga+ers

Source: /earst (6;)


43/98

>nsu!erised DiscourseSegmentation

@nsupervisedR uses no trainingdata

2y!ically cohesion-based: segment

te*t into subto!ics in wicsentences!aragra!s are cohesivewit eac oter

Cohesion: use of linguistic deicesto establis links between te*tualunits

Lexical Cohesion: use of same or

similar (e.g. y!ernyms y!onyms


44/98

2e*tiling (/earst 6;)

&n unsu!erised coesion basedalgoritm com!are adBacent blocks of te*t

look for sifts in ocabulary

2ree main ste!s

2okenization Le*ical score determination

0oundary identiEcation


45/98

2e*tiling (/earst 6;)sentence numbers

erm freuency


46/98

2e*t2iling: Pre3!rocessing

5onert te*t stream into terms (words)

#emoe sto!3words $.g. Cte Ca Cof 7

#educe eac word to its root form (in8ectionalmor!ology) Nouns: singular to !lural (s!orts 3 s!ort) Terbs: in8ected to base form (coming 3 come)

Diide te*t into toen se.uences(pseudo*sentences)of eual lengt (say ,- words)


47/98

2e*t2iling: Pre3!rocessing

5onert te*t stream into terms (words)

#emoe sto!3words $.g. Cte Ca Cof 7

#educe eac word to its root form (in8ectionalmor!ology) Nouns: singular to !lural (s!orts 3 s!ort) Terbs: in8ected to base form (coming 3 come)

Diide te*t into toen se.uences(pseudo*sentences)of eual lengt (say ,- words)

l


48/98

l

sco

rede

t

5om!ute lexical cohesion scoreateac ga!

Similarity of te blocks before and afterte ga!

$ac block is made of k !seudo3

sentences 5osine similarity between te blocks1

word ectors

4a!

l


49/98

l

sco

rede

t

5om!ute lexical cohesion scoreateac ga!

Similarity of te blocks before and afterte ga!

$ac block is made of k !seudo3

sentences 5osine similarity between te blocks1

word ectors

4a!

Similarity

2 t2ili 0 d


50/98

2e*t2iling: 0oundaryidentiEcation 5om!ute te de!t scores of eac

ga! Distance from te !eaks on bot sides:

(a3b)O(c3b) &ssign segmentation if te de!t

score is larger tan a boundary cutoU

(e.g. ag3sd)ab

calley

2 t2ili 0 d


51/98

2e*t2iling: 0oundaryidentiEcation 5om!ute te de!t scores of eac

ga! Distance from te !eaks on bot sides:

(a3b)O(c3b) &ssign segmentation if te de!t

score is larger tan a boundary cutoU

(e.g. ag3sd)


52/98

2e*tiling (/earst 6;)

Source:/earst

(6nsu!erised based on cue !rases (or

discourse markers) Su!erised based on discourse treebanks Q cf.

te Penn Discourse 2reebank(tt!:www.seas.u!enn.eduZ!dtb)

&utomatic 5oerence


64/98

&utomatic 5oerence&ssignment Sallow cue3!rase3based algoritm:

6. "dentify cue !rases in a te*t

,. Segment te*t into discourse segmentsusing cue !rases

F. &ssign coerence relations between

consecutie discourse segments


65/98

Automatic co#erence assignment

Ste! 6: identiEcation of cue !rases tat signalcoerence relations

iscourse connectives: Cbecause CaltougCe*am!le Cwit Cand 7

5onnecties are ambiguous Git its distant orbit ars e*ibits frigid weater

conditions Ge can see ars wit an ordinary telesco!e

>se some sim!le euristics e.g. ca!italization ofwit etc. or more com!le* disambiguationtecniues


66/98

Automatic co#erence assignment

Ste! ,: segment te te*t into discoursesegments2y!ically sentences may suKce 0>2

clauses are often more a!!ro!riate(S!orleder and La!ata ,--


67/98

"dentifying #S &utomatically(arcu 6)

& su!erised !arser trained on a discoursetreebank - retorical structure trees and3annotated

for retorical relations (##s)

$lementary discourse units ($D>s) linked by##s Parser learns to identify Nucleus Satellite and

teir ## Aeatures: Gordnet3based similarity le*ical

structural

>ses discourse segmenter to identifydiscourse units2rained to segment on and3labeled cor!us

(5


68/98


Dialogue2urn3taking

S!eec act



0ot &na!ora

5o3reference


69/98

"D$N2"A="N4 G/"5/

*&$IS#$A$# 2@2/$ S&$(D"S5@>#S$) &$I$%

2ask deEnition

5ains of mentions in te*t


70/98

5ains of mentions in te*t(5@#$A$#$N5$ 5/&"N)

2oni Jonson!ulls a ta!e measure across te front of wat wasonce a stately Tictorian ome.& dee! trenc now runs along its nort wall e*!osed wente ouselurced two feet oU itsfoundation during lastweek[s eartuake.@nce inside ses!ends nearly four ours measuring anddiagramming eac room in te I-3year3old ouse gateringenoug information to estimate wat it would cost to rebuild

it.Gile seworks inside a tenant returns wit seeral friendsto collect furniture and cloting.@ne of te friends swee!s broken dises and sattered glassfrom a counterto! and starts to !ack wat can be salaged

from te kitcen. (GSJ section of Penn 2reebankcor us


71/98

&na!ora \ 5oreference

5@#$A$#$N2 not &N&P/@#"5 two mentions of same obBect in diUerent

documents @bamawas interiewed last nigt.2e

President..

&N&P/@#"5 not 5@#$A$#$N2

identity of sense: Jon bougt a sirt and 0illgot @N$ too

Nominal ana!oric


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Nominal ana!orice*!ressions

#$AL$+"T$ P#@N@>NS: Jon bougt imself an amburger

P#@N@>NS: DeEnite !ronouns: #oss bougt ]a radiometer ^ tree

kilograms of after3dinner mints_ and gae ]it ^ tem_ toNadia for er birtday. (/irst 6I6)

"ndeEnite !ronouns: Sally admired Sue1s Backet so segot one for 5ristmas. (4arnam ,--6)

D$A"N"2$ D$S5#"P2"@NS:

& man and a woman came into te room. 2e man satdown.

$!itets: & man ran into my car. 2e idiot wasn1t lookingwere e was going.

D$@NS2#&2"T$S:

2om as been caugt so!lifting. 2at boy will turn out


73/98

Non3nominal ana!oric e*!ressions

P#@3T$#0S: Daryel tinks like " do.

4&PP"N4:

Nadia brougt te food for te !icnic and Daryel `te wine.

2$P@#&L #$A$#$N5$S: "n te mid3Si*ties free loe was ram!ant across

cam!us. "t was ten tat Sue turned to Scientology.(/irst 6I6)

L@5&2"T$ #$A$#$N5$S:

2e 5urc of Scientology met in a secret roombeind te local 5olonel Sanders1 cicken stand.

Sue ad er Erst dianetic e*!erience tere. (/irst


74/98

2y!es of ana!oric relations

"dentity of #$A$#$N5$ #oss bougt ]a radiometer ^ tree kilograms of after3

dinner mints_ and gae ]it ^ tem_ to Nadia for erbirtday.

"dentity of S$NS$ Sally admired Sue1s Backet so se got one for

5ristmas. (4arnam ,--6) (P&=5/$5? P#@N@>NS): 2e man wo gae is

!ayceck to is wife is wiser tan te man wo gae itto is mistress. (?arttunen 6;H9)

0@>ND ana!ora No "talian beliees tat Gorld 5u! referees treated is

team fairly

&SS@5"&2"T$ indirect ana!oric relations(Ybridging1)

2e ouse 7. te kitcen

&ssociatie ana!ora


75/98

&ssociatie ana!ora(a ty!e of 0#"D4"N4)

2oni Jonson!ulls a ta!e measure across te front of wat wasonce a stately Tictorian ome.& dee! trenc now runs along its nort wall e*!osed wen te

ouse lurced two feet oU itsfoundation during last week[seartuake.@nce inside ses!ends nearly four ours measuring anddiagramming eac room in te I-3year3old ouse gateringenoug information to estimate wat it would cost to rebuild it.Gile seworks inside a tenant returns wit seeral friends tocollect furniture and cloting.@ne of te friends swee!s broken dises and sattered glassfrom a counterto! and starts to !ack wat can be salagedfrom te kitcen.

(GSJ section of Penn 2reebankcor us

Not all Yana!oric1 e*!ressions


76/98

Not all ana!oric e*!ressionsalways ana!oric

$*!leties "t is alf !ast two.2ere is an engine at &on

Airst mention deEnites #eferences to isual situation(Ye*o!ora1) !ick tat u! and !ut it oer tere.

"nter!reting ana!oric


77/98

"nter!reting ana!orice*!ressions

"nter!reting (Yresoling1) an ana!orice*!ressions inoles at least treeas!ects:6. Deciding weter te e*!ression is in fact

ana!oric

,. "dentifying its antecedent (!ossibly notintroduced by a nominal)

F. Determining its meaning (cfr. identity ofsense s. identity of reference)

(not necessarily taken in tis order)

Aactors tat aUect te inter!retation


78/98

Aactors tat aUect te inter!retationof ana!oric e*!ressions

Aactors: or!ological features (agreement)

Syntactic information (0inding)

Salience Le*ical and commonsense knowledge

Distinction often made between

5@NS2#&"N2S (must e.g. agreementin gender as:,ohn---he " the book---it)and P#$A$#$N5$S

Su!erised learning for coreference


79/98

5lassiEcation2rain a classiEer to determine weter

two mentions are coreferentor not

coreferent

.Israel/ will as0 the 1S to 2 .the 3ewish state/ 2 .Ira$i/4 +++

coref 9

notcoref 9

coref 9

Su!erised learning for coreferenceresolution

Su!erised learning for coreference


80/98

5lustering !airwise coreferencedecisions

Israel

the Bewishstate

its

A #itterypu"lic

5lustering&lgoritm

Ira.

Ira.i

@S

@nited States

Ira.

@SA

M"srael

will ask te >S

Mte Jewis state

M"rai

;;;

coref

not coref

not

coref

Israel

Su!erised learning for coreferenceresolution

S t l A t


81/98

Soon et al. (,--6): Aeatures

Aeatures >sed Number &greement Aeature: are i and B bot singular or

bot !lural

Semantic 5lass &greement Aeature: true if te sem classof i and B are te same or if one is te !arent of teoterX false or unknown oterwise

4ender &greement Aeature: are i and B of te samegender based on designator (r.) or !ronoun

0ot3Pro!er3Names Aeature: true if i and B are !ro!er

names &lias Aeature: true if i is an alias of B or ice ersa

&!!ositie Aeature: true if i or B is a !ro!er name and iand B are se!arated by a comma and no erb

Soon et al (,--6): semantic class


82/98

Soon et al. (,--6): semantic classagreement

P$#S@N

A$&L$ &L$

@0J$52

D&2$@#4&N"&2"@N

2"$ @N$= P$#5$N2

L@5&2"@N

S$5L&SS R true iU semclass(i) VR semclass(B) oriceersa

S t l A t


83/98

Soon et al. (,--6): Aeatures

$*am!le V"srael te Jewisstate

Le*ical and commonsense


84/98

Le*ical and commonsenseknowledge

Nominals te most common ty!e ofana!oric e*!ression

ain source of errors wit nominals:

lack of commonsense knowledge Semantics as been !ointed out as being

releant since seminal work: 5f. e.g. 5arniak (6;F)X /obbs (6;I)



85/98

Le*ical and commonsenseknowledge: e*am!les

Gic kind of semantics is useful forcoreference resolution9 synonymy relations

i.e. diUerent e*!ressions used to refer tote same conce!t

Synonymy relations and coreference


86/98

Synonymy relations and coreferenceresolution

2oni Jonson !ulls a ta!e measure across te front ofwat was once Ma stately Tictorian ome.

7..

2e remainder of Mte ouseleans !recariouslyagainst a sturdy oak tree.

to merge teir >.S. satellite 2T o!erations wit

Primestar Partners Mte nation1s largest satellite 2Tcom!any sources familiar wit te inuiry say. M. . .

2e slot is igly aluable because it is one of onlytree aailable in Mtis countryfrom wic a satellitecan beam 2T !rograms across most of Nort &merica

simultaneously.



87/98


Gic kind of semantics is useful forcoreference resolution9 instance*of relations

i.e. tat an indiidual belongs to acertain class (e.g. @bama is an istanceof >.S. President Aido is an instance ofdog "taly is an istance of Nation)

"nstantiation relations and


88/98

"nstantiation relations andcoreference resolution

M2e A55 took Mtree s!eciEcactions regarding M&2'2. 0y a


89/98


Gic kind of semantics is useful forcoreference resolution9 isa relations

i.e. subsum!tion between conce!ts (catis3a feline car is3a eicle)

Subsum!tion relations and


90/98

Subsum!tion relations andcoreference resolution

MPetrie Stores 5or!oration Secaucus NJ said anuncertain economy and faltering sales !robablywill result in a second uarter loss and !era!s

a deEcit for te Erst si* monts of Escal 6#&L L&N4>&4$ "S P$#T&S"T$


98/98

$7& &

discourse dialogue

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