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25.08.16

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DiscourseStructureinTwi6erConversa:ons

ManfredStede,UnivPotsdamESSLLI2016

Overview

•  Twi6erconversa:ons??•  Fromspeechactsviadialogactstotweetacts•  Coherencerela:onsand„rhetoricalstructure“•  Derivingrhetoricalstructureautoma:cally•  RhetoricalstructureinTwi6erconversa:ons

ManfredStedeESSLLI2016

Twi6erConversa:ons

•  reply-to-func:oncreatesconversa:onsonTwi6er

•  ~20-25%oftweetsarereplies•  ~40%oftweets=partofconversa:ons•  treestructure:


Analysis:Whatdopeopledo?

WTF? I have green energy and am supposed to finance nuclear and coal? What nonsense. WHAT NONSENSE!

wtf? Why and in which way? Oh, and I have coal and nuclear energy and have to finance green power thanks to [new law] just so you can have it cheaper? WTF!

…

Agree. But it’s also a fact that …

right, I’m destroying the climate. Put a windmill in front of your house and we’ll talk. …

…


Speechacts•  JohnAus:n:Howtodothingswithwords(1962)

•  Performa:veu6erancesdonotmerelydescribetheworldbutchangeit(anddeterminingtheirtruthvalueispointless)

•  Inamethisshipthe„QueenElizabeth.“•  Igiveandbequeathmywatchtomybrother.•  Ibetyousixpenceitwillraintomorrow.•  Iapologize.


Speechacts

•  JohnAus0n:Howtodothingswithwords(1962)

•  Levelsofanalyzinganu6erance:– locu0on:performingtheac:onofu6ering– illocu0on:theac:on/inten:onofthespeaker– perlocu0on:theeffectoftheu6eranceontheaddressee


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SpeechActs•  JohnSearle:Speechacts(1969);Ataxonomyofillocu:onary

acts(1975)

•  Asser0ves=speechactsthatcommitaspeakertobelievingtheexpressedproposi:on

•  Direc0ves=speechactsthataretocausethehearertotakeapar:cularac:on,e.g.requests,commandsandadvice

•  Commissives=speechactsthatcommitaspeakertodoingsomefutureac:on,e.g.promisesandoaths

•  Expressives=speechactsthatexpressthespeaker'sagtudesandemo:onstowardstheproposi:on,e.g.congratula:ons,excusesandthanks

•  Declara0ons=speechactsthatchangethesocialsphere,e.g.bap:smsorpronouncingsomeonehusbandandwife


Speechacts,empirically

Hugeliteratureonspeechacttheory:theirdefini:on;linguis:crealiza:on;indirectspeechacts,...Inprac0ce,thevastmajorityofillocu:onsencounteredintextareasser0ves


Example:User-generatedhotelreviews

IstayedatthisHiltonforthethird:me.Asusual,thestaffwasextremelya6en:veandfriendly.TheconciergeiscalledPierre;healwayswearsabow:e.He‘spar:cularlysweet,evenearlyinthemorning.Iguesshe‘sgegngverygoodcoffeeathisplace....


Aninventoryofillocu:onsforsubjec:vetext

•  Report:Thewaiterbroughtusanewcocktail.•  Report_author:Ipayedforitrightaway.•  Ident:Iwasafraidtoseehimagain.•  Evalua:on:Thecocktailturnedouttobelousy.•  Es:mate:Itprobablywasmadeinahurry.•  Commitment:I‘llneverhaveitagain!•  Direc:ve:Andyoushouldavoidit,too.

M.Stede,A.Peldszus:Theroleofillocu:onarystatusintheusagecondi:onsofcausalconnec:vesandincoherencerela:ons.JournalofPragma:cs44(2),2012

Dialogact

•  ...capturesthefunc:onalrelevanceofanu6eranceincontext

•  ExamplefromVerbmobilappointmentscheduling(Alexanderssonetal.1997)– Canwemeetinthesecondhalfofmay?– Well,that‘snotsogood,becauseI‘llbeonholiday.HowaboutearlyJune,suchasthe3rd?

– Hm,Idon‘tknow,allright,IguessIcandothat.

SUGGEST-DATE

REJECT

GIVE-REASON

SUGGEST-DATE

HESITATE

ACCEPT-DATE


Dialogact

•  SomeproposalsofDAtaxonomies– DAMSL(Allen&Core,1997)– DIT++(Bunt2006)h6p://dit.utv.nl

•  Automa:cDArecogni:on–  (Stolckeetal.00)onminutesofmee:ngs– niceoverview:P.Kral:Dialogueactrecogni:onapproaches.Compu:ngandInforma:cs29:227-250,2010


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WhystudythisonTwi6er?•  Intheconversa:ons,dopeople

–  talktoeachotherorpasteachother?–  exchangeinforma:on?–  exchangeopinions?–  exchangearguments?–  displaytheiremo:ons?–  follow„standard“dialogprotocols?–  ...

•  Knowingthisisrelevant,interalia,forbuildinggoodTwi6erbots–  User:QUESTION

Bot:ANSWER–  User:OPINION

Bot:AGREE|DISAGREE–  ...


Fromdialogactsto„tweetacts“

•  Tweetscollectedonthetopicofrenewableenergy– keywordspogng–  re-crawlmissingtweetstogettreesascompleteaspossible

•  1566tweetsin172conversa:ons

E.Zarisheva,T.Scheffler:Dialogactannota:onforTwi6erconversa:ons.Proc.ofSIGDIAL,Prague,2015


DAtaxonomy(part1)


DAtaxonomy(part2)


Annota:on

•  Total:51DAlabels•  Annotatorshavetochooseonelabelpertweetsegment

•  [True,unfortunately.]Agreement[Butwhatabouttherealiza:onofhighsolarac:vityinthe70sand80s?]SetQues:on


Annota:on

•  minimally-trainedundergraduatestudents•  twosteps–  segmenta:on:Fleissmul:-pi0.88– DAlabelling:Fleissmul:-pi0.56

•  no:ce:disagreementsonsubcategoriesarepunishedlikedisagreementsonmaincategories

•  (with10DAs:mul:-pi0.76)•  mostdisagreementisamongtypesofInforma:on-providing

•  Finally,allannota:onsmergedintoasinglegold-standard:1213tweets/2936segments


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Tweet-internalstructure

[@TheBug0815@Luegendetektor@McGeiz]0[Exactly,wedon‘tneedabaseload,it‘sonlyacapitalistconstruct]Agreement–[Wind/PVaresufficient?]PropQues:on[Lol]Disagreement


Automa:crecogni:on

•  Assumegoldsegments•  Splitconversa:ontreesintosinglestrands

QUESTION

ANSWER1 ANSWER2

DISAGREEMENT AGREE THANK

TatjanaSchefflerandElinaZarisheva:Dialogactrecogni:onforTwi6erconversa:ons.Proc.oftheLRECWorkshoponNormalisa:onandAnalysisofSocialMediaTexts,2016

Features

•  userdefined(UD):–  segmentlength–  author–  posi:onofsegmentintweet–  presenceofques:onmarks,links,hashtags,etc.

•  top50/100wordsforthedialogact(byTF-IDF)

•  wordembeddings(pre-calculated,64dimensions)

feature combinationsUD

UD + top50UD + top100

UD + embeddingsUD + top100 +

embeddings

Experiment:FullDAsetHiddenMarkovModel

Mul:nomialdistribu:on(Discretevalues)Gaussiandistribu:on(M-dimensionalvectors)

Condi:onalRandomFieldsmajoritybaseline(INFORM)f=0.09

Results:Reduced/MinimalDAset

previouswork:–  (Zhangetal.,2011):F1=0.695for5classes–  (ArguelloandShaffer,2015)MOOCforumposts:averageprecision~0.65;ours,0.70

–  (VosoughiandRoy,2016):F1=0.70for6classes

F1 Baseline GHMM CRFFull (50 DAs) 0.09 0.21 0.31Reduced (12 DAs) 0.16 0.36 0.51Minimal (8 DAs) 0.34 0.51 0.72

Overview



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Coherence

JohntookatraintoIstanbul.Hehasfamilythere.JohntookatraintoIstanbul.Helikesspinach.(Hobbs76)

Coherence:=Coreference+Coherencerela:ons


Coherencerela:ons

•  JohntookatraintoIstanbul.HefirstvisitedtheHagiaSophia.(temporal-sequence)

•  JohntookatraintoIstanbul.HissisterwenttoRome.(contrast)

•  JohntookatraintoIstanbul.ItwasacomfortableEurocity.(elabora:on)


Claim

•  Intext,coherencearisesbecauseeveryclauseislinkedviaacoherencerela:ontoitsle~context.

•  OK,but...– howexactlydoyoudeterminetheminimalunits?– whatinventoryofrela:onsdoyouassume?– whatcanthecurrentclausebea6achedto?


Rhetorical Structure Theory (Mann/Thompson 88)

9-14

Reason

In manchenFällen ist ihrEinfluss auf

höchster Ebenewichtig .

10-14

Elaboration

Das giltbesonders beiProjekten , dieder Bund selbst

in denRegionen

vorantreibt ,

11-14

E-elaboration

genannt seienzum Beispiel

dieUmgehungsstra

ßen und dasVerkehrsprojek

t DeutscheEinheit Nr. 17 -

derHavelausbau .

12-14

Elaboration

Letzterer ist imPotsdamer

Umlandumstritten .

13-14

Elaboration

OhneÜbertreibung

lässt sich sagen, dass die

Gegner desProjekts klar inder Mehrheit

sind .

JointFür sie muss

die Haltung derWahlkreiskandi

daten zudiesem Thema

auch einpolitisches

Signal sein .

Joint

2-7

Reason

5-7

6-7Die Einwohnervon Städten

und Gemeindenerfahren meisterst kurz vor

dem Entscheidauf

Bundesebene ,wer ihr direkter

politischerAnsprechpartner in Berlin sein

könnte .

Cause

WelcheHaltungen sie

zu spezifischenörtlichen

Problemeneinnehmen ,

bleibt für vieleWähler

zunächst imDunkeln .

Dann ist es oftschon zu spät ,die inhaltlichenSchwerpunkteder Kandidatenzu bestimmen .

Reason

2-4

Background

Derzeitbestimmen dieParteien , wen

sie in denWahlkreisen

der Region fürdie

Bundestagswahl 2002 in das

Rennenschickenwerden .

3-4

Elaboration

Dies geschiehteher beiläufig ,

zumindestohne

Beteiligungeiner breitenÖffentlichkeit .

Elaboration

Sich frühzeitigmit den

Vorstellungender

Bundestagsaspiranten vertrautzu machen , ist

deshalbsinnvoll .

2-14


Overview



Twono:onsofdiscourseparsing

•  „Shallow“–  RootedinthePennDiscourseTreebankcorpus–  Assignargumentstoconnec:ves–  [Otherreviewerssaidit‘sagreatplace,]Arg1but[myimpressionwasotherwise.]Arg2

–  Iden:fyotherrela:onsandargumentsbetweensentences•  „Fullstructure“–  Buildacompletestructureforthetext–  RhetoricalStructureTheory(Mann/Thompson88)–  SegmentedDiscourseRepresenta:onTheory(Asher/Lascarides03)


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RSTDiscourseTreebank•  385ar:clesfromWallStreetJournal•  OverlapwiththePennTreebank

•  78rela:ons,manyofthemarisingfromnuclearityreversals– Evalua:on(nucleus:evaluated/sat:evalua:ng)– Evalua:on(nucleus:evalua:ng/sat:evaluated)


L.Carlsonetal.:Buildingadiscourse-taggedcorpusintheframeworkofRhetoricalStructureTheory.Proc.ofSIGDIAL,2001

HILDA:RST-parsingviaSVMclassifica:on


H.Hernaultetal.LBuildingaDiscourseParserUsingSupportVectorMachineClassifica:on.Dialogue&Discourse1(3),2010

HILDA:RST-parsingviaSVMclassifica:on

•  Training&Test:RSTDiscourseTreebank•  Reduce78rela:onsto18„families“•  AQribu:on,Background,Cause,Comparison,Condi:on,Contrast,Elabora:on,Enablement,Evalua:on,Explana:on,Joint,Manner-Means,Summary,Temporal,Topic-Change,Topic-Comment,Same-unit,Textual-organiza:on

•  Anyn-arytreeswithn>2areconvertedtobinarytrees


Idea

•  Complexity:linearwithrespecttolengthofinputtext

•  Bothsegmenta:onandrela:onlabellingrunassupervisedclassifica:ontasks,usingsupportvectormachines

•  (Here,weskipthesegmenta:onstep(F-score0.95)ManfredStedeESSLLI2016

Idea

•  STRUCTclassifier:Whatistheprobabilityofsegmentsi,i+1beingconnected?

•  LABELclassifier:Whatisthemostlikelyrela:ontoholdbetweensegmentsi,i+1?


STRUCTclassifier

Seg1Seg2Seg3Seg4Seg5Seg6elabNS(Seg1,Seg2)condNS(Seg4,Seg5)

evalSN(Seg3,condNS(Seg4,Seg5)

list(elabNS(Seg1,Seg2),evalSN(Seg3,condNS(Seg4,Seg5)))

REL(list(elabNS(Seg1,Seg2),evalSN(Seg3,condNS(Seg4,Seg5))),Seg6)


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RELclassifier

Seg1Seg2Seg3Seg4Seg5Seg6elabNS(Seg1,Seg2)condNS(Seg4,Seg5)

evalSN(Seg3,condNS(Seg4,Seg5)

list(elabNS(Seg1,Seg2),evalSN(Seg3,condNS(Seg4,Seg5)

elabNS(list(elabNS(Seg1,Seg2),evalSN(Seg3,condNS(Seg4,Seg5))),Seg6)


Featuresforrela:onlabeling(1)


Featuresforrela:onlabeling(3)

Results:Rela:onlabeling

•  STRUCT– Trainedon52.683instances(1/3posi:ve)– Testedon8.558instances– Featurespacedimensionality:136.987– Accuracywithpolyn.Kernel:85.0

•  LABEL– Trainedon17.742instances– Testedon2.887instances– Accuracyofmul:-classSVM:66.8


Performanceonindividualrela:ons

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Overview



Inter-Tweet-Rela:ons •  UdoSieverding

#Offshore-Ausbau: Warum schweigen Dauer-#EEG-Kritiker @Der_BDI @iw_koeln @insm @DICEHHU @RolandTichy @bdew_ev @igbce? http://t.co/WfZsirxMiC

•  mapro67 @UdoSieverding weil sie alle Interessen der großen Stromkonzerne vertreten @Der_BDI @iw_koeln @insm @DICEHHU @RolandTichy @bdew_ev @igbce

•  UdoSieverding

#Offshore expansion: Why are the big critics of the renewable energy law so quiet?

•  mapro67 @UdoSieverding because they represent the interests of the big energy companies


Elaboration Antithesis

Reason Elaboration


Antithesis

Sequence

Ongoingwork

Elaboration Antithesis

Reason Elaboration


Antithesis

Sequence

Tobeclarified:DA≈CoherenceRel

Inform

Inform

SetQuestion

Inform

Disagree Suggest

Request

Promise

Add:Tweet-internalstructure


•  WTF? I have green energy and am supposed to finance nuclear and coal? What nonsense. WHAT NONSENSE!

•  Agree. But it’s also a fact that …

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