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Dialogue Structure in MicrotextAAAI-11 Workshop on Analyzing Microtext
Micha Elsner
School of InformaticsUniversity of Edinburgh
8 August, 2011
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Unconventional interactions
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Where do we fit in?
Speech
I Prosody, tone of voiceI No latencyI Short utterancesI Strictly sequential
Text
I Just wordsI Ultra-high latencyI Long documentsI Hierarchical / searchable
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Interfaces matter!
I Mostly text, some multimediaI Low latency (IM) vs high latency (web forum)I Short turns (twitter) vs long turns (email)I Sequential (Huffington comments) vs structured (Slashdot
comments)
These differences affect the language we see!
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Overview
Introduction
Case study: IRC chat
IRC vs speech
Text and speech models for disentangling IRC
Conclusions
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Speech: conversational structure
I One speaker at a timeI Has the floor (Sacks et al)
I Speaker signals intent tokeep talking or finish
I Coordination via shortutterances:
I Filled pauses “uh”,backchannels “yeah”
(Fox, Sudderth et al: A Sticky HDP-HMM)
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Turn-taking in IRC chat
0 10 100 10000
20
40
seconds
Fre
quen
cy
I More tolerant of long pausesI And possibly of “interruption”
I Some backchannel-like utterances:I ∼ 10% one-word comments: “lol”, “ok”I Switchboard: ∼ 17% backchannels: “yeah”, “uh-huh”
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Turn-taking in IRC chat
0 10 100 10000
20
40
seconds
Fre
quen
cy
I More tolerant of long pausesI And possibly of “interruption”
I Some backchannel-like utterances:I ∼ 10% one-word comments: “lol”, “ok”I Switchboard: ∼ 17% backchannels: “yeah”, “uh-huh”
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Multiple floorsUsually several conversations at a time
I Between 2 and 3 active during each utterance
Chatters participate in many conversationsI The more one speaks, the more threads they speak in
0 10 20 30 40 50 60Utterances
0
1
2
3
4
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Threads
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Questions
What information is useful?How well do text/speech models adapt?
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Disentanglement (threading)
(Elsner+Charniak ACL 08, CL 10, ACL 11)
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Preliminaries
Six annotators marked 800 lines of chatI From a Linux tech support forum on IRC
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An initial model
Correlation clustering framework:I Classify each pair of utterances as “same thread” or
“different thread”I Partition the transcript to keep “same” utterances together
and split “different” ones apartI NP-hard, so we use heuristics
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An initial model
Correlation clustering framework:I Classify each pair of utterances as “same thread” or
“different thread”I Partition the transcript to keep “same” utterances together
and split “different” ones apartI NP-hard, so we use heuristics
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Classifying utterances
Pair of utterances: same conversation or different?
Chat-based features (F 66%)I Time between utterancesI Same speakerI Speaker’s name mentioned
Discourse features (F 58%)
Word overlap (F 56%)
Combined model (F 71%)
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Classifying utterances
Pair of utterances: same conversation or different?
Chat-based features (F 66%)
Discourse features (F 58%)I Questions, answers, greetings, etc.
Word overlap (F 56%)
Combined model (F 71%)
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Classifying utterances
Pair of utterances: same conversation or different?
Chat-based features (F 66%)
Discourse features (F 58%)
Word overlap (F 56%)I Weighted by word probability in corpusI Simplistic coherence feature
Combined model (F 71%)
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Classifying utterances
Pair of utterances: same conversation or different?
Chat-based features (F 66%)
Discourse features (F 58%)
Word overlap (F 56%)
Combined model (F 71%)
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Assigning a single sentence
It’s easy to maximize the objective locally...I Even though the global problem is hard
AccuracySame as previous 56Corr. Clustering 76
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Models from text and speech
Models which may apply here...I Initially designed for putting sentences in orderI Distinguish coherent sequence of utterances from
randomnessI Many different aspects of languageI Not all our own work.
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Entity grid
Model of transitions from sentence to sentence(Lapata+Barzilay ‘05,Barzilay+Lapata ‘05):
Text Syntactic roleSuddenly a White Rabbit ran by her. subject
Alice heard the Rabbit say “I shall be late!” objectThe Rabbit took a watch out of its pocket. subjectAlice started to her feet. missing
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Topical entity grid
Relationships between different words“a crow infected with West Nile...”“the outbreak was the first...”
Our own work.
I Represents words in a “semantic space”: LDA (Blei+al ‘01)I Entity-grid-like model of transitionsI “Semantics” can be noisy...
I More sensitive than the Entity Grid, but easy to fool!
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IBM Model 1
Single sentence of contextLearns word-to-word relationships directly
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Pronouns
Detect passages with stranded pronouns:(Charniak+Elsner ‘09), (Elsner+Charniak ‘08)
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Old vs new information
New information needs complex packaging“Secretary of State Hillary Clinton”
Old information doesn’t“Clinton”
Soft constraints: put the “new”-looking phrasefirst(Elsner+Charniak ‘08) following (Poesio+al ‘05)
Works well for news, poorly on speech and chatI Entities introduced in different ways
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Old vs new information
New information needs complex packaging“Secretary of State Hillary Clinton”
Old information doesn’t“Clinton”
Soft constraints: put the “new”-looking phrasefirst(Elsner+Charniak ‘08) following (Poesio+al ‘05)
Works well for news, poorly on speech and chatI Entities introduced in different ways
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Synthetic speech transcripts
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Speech results
Chance 50Corr. Clustering 76EGrid 77Topical EGrid 78IBM-1 69Pronouns 52Time 58Combined 83
I Coherence approach outperforms previous
I Topical model is usefulI Pronouns very bad
Best models: sensitive, many-sentence context
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Speech results
Chance 50Corr. Clustering 76EGrid 77Topical EGrid 78IBM-1 69Pronouns 52Time 58Combined 83
I Coherence approach outperforms previousI Topical model is useful
I Pronouns very bad
Best models: sensitive, many-sentence context
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Speech results
Chance 50Corr. Clustering 76EGrid 77Topical EGrid 78IBM-1 69Pronouns 52Time 58Combined 83
I Coherence approach outperforms previousI Topical model is usefulI Pronouns very bad
Best models: sensitive, many-sentence context
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Speech results
Chance 50Corr. Clustering 76EGrid 77Topical EGrid 78IBM-1 69Pronouns 52Time 58Combined 83
I Coherence approach outperforms previousI Topical model is usefulI Pronouns very bad
Best models: sensitive, many-sentence context
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Pronominals in speech and text
Different usage patterns...Corpus Deictics Pronouns 3rd person pronounsWSJ .04 0.64 0.52Switchboard .12 1.18 0.39IRC .09 0.92 0.31
News models totally inadequate here...I Microtext also differs from speech pattern
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IRC results
Chat-specific 74
Corr. Clustering 76Chat+EGrid 79Chat+Topical EGrid 77Chat+IBM-1 76Chat+Pronouns 74
I Coherence still outperform previousI Lexical models not as good
I Lack of data: trained on phone conversations
I Pronouns same as before
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IRC results
Chat-specific 74Corr. Clustering 76
Chat+EGrid 79Chat+Topical EGrid 77Chat+IBM-1 76Chat+Pronouns 74
I Coherence still outperform previousI Lexical models not as good
I Lack of data: trained on phone conversations
I Pronouns same as before
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IRC results
Chat-specific 74Corr. Clustering 76Chat+EGrid 79
Chat+Topical EGrid 77Chat+IBM-1 76Chat+Pronouns 74
I Coherence still outperform previous
I Lexical models not as good
I Lack of data: trained on phone conversations
I Pronouns same as before
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IRC results
Chat-specific 74Corr. Clustering 76Chat+EGrid 79Chat+Topical EGrid 77Chat+IBM-1 76
Chat+Pronouns 74
I Coherence still outperform previousI Lexical models not as good
I Lack of data: trained on phone conversations
I Pronouns same as before
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IRC results
Chat-specific 74Corr. Clustering 76Chat+EGrid 79Chat+Topical EGrid 77Chat+IBM-1 76Chat+Pronouns 74
I Coherence still outperform previousI Lexical models not as good
I Lack of data: trained on phone conversationsI Pronouns same as before
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More data
800 utterances not enough for you?I Much larger corpora from (Martell+Adams ‘08)
I Using our annotation software and protocolI ∼ 20000 total utterances from three newsgroups
M+A corporaCorr. Clustering 89EGrid 93
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Full-scale disentanglement
Unfortunately, scalability problems withadvanced models...
I So results only for simple model
Annotators
Best Baseline Corr. Clustering
Agreement 53
35 (Pause 35) 41
a
Best overall result: (Wang+Oard ‘09) 47
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Full-scale disentanglement
Unfortunately, scalability problems withadvanced models...
I So results only for simple model
Annotators Best Baseline
Corr. Clustering
Agreement 53 35 (Pause 35)
41
a
Best overall result: (Wang+Oard ‘09) 47
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Full-scale disentanglement
Unfortunately, scalability problems withadvanced models...
I So results only for simple model
Annotators Best Baseline Corr. ClusteringAgreement 53 35 (Pause 35) 41
a
Best overall result: (Wang+Oard ‘09) 47
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Full-scale disentanglement
Unfortunately, scalability problems withadvanced models...
I So results only for simple model
Annotators Best Baseline Corr. ClusteringAgreement 53 35 (Pause 35) 41
a
Best overall result: (Wang+Oard ‘09) 47
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What we’ve learned
IRC chat is like speechI Turn-taking and floor controlI Models based on lexical/entity coherence
I But resource-poor (should be surmountable)
Real differences existI Floors more fluidI Referring behavior
I Full NPsI Pronominals
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Conclusions
Chat disentanglementI Sophisticated models can help!I Still technical problems
I Scaling inference, building topic models...I Some real differences from speech
I Coreference is a new challenge
MicrotextI Interface determines communication behaviorI May vary from any previous mode of communication
I Important to consider before applying off-the-shelf models
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Conclusions
Chat disentanglementI Sophisticated models can help!I Still technical problems
I Scaling inference, building topic models...I Some real differences from speech
I Coreference is a new challenge
MicrotextI Interface determines communication behaviorI May vary from any previous mode of communication
I Important to consider before applying off-the-shelf models
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Thanks!
Thanks to...I Eugene Charniak, Mark Johnson, Regina BarzilayI Former labmates at Brown UniversityI Google Fellowship for NLPI Craig Martell for NPS dataset
Corpus and software availablecs.brown.edu/∼ melsnerbitbucket.org/melsner/browncoherence
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One-to-one overlap
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One-to-one overlap
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One-to-one overlap
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One-to-one overlap
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