Unsupervised Counselor Dialogue Clustering for
Positive Emotion Elicitation in Neural Dialogue System
Nurul Lubis, Sakriani Sakti, Koichiro Yoshino, Satoshi Nakamura
Information Science Division, Nara Institute of Science and Technology, JAPAN

13 July 2018

Nurul Lubis

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Affective dialogue systems

High potential of dialoguesystems to address theemotional needs of users• Increase of dialogue system works andapplications in various tasks involving
affect• Companion for the elderly[Miehle et al., 2017]• Distress clues assessment[DeVault et al., 2014]• Affect-sensitive tutoring[Forbes-Riley and Litman, 2012]13 July 2018

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Emotion elicitation
Traditional emotion works

Intent foremotion
elicitation

Expression

Recognition
Expression

Emotion elicitation: eliciting change of emotion in dialogue• Using machine translation with target emotion (Hasegawa et al., 2013)• Using system’s affective personalities (Skowron et al., 2013)
 Have not yet considered the emotional benefit for the user
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Positive emotion elicitation
We aim to draw on an overlooked
potential of emotion elicitation toimprove user emotional states• A chat-based dialogue systemwith an implicit goal of positive
emotion elicitation
Circumplex model of affect [Russell, 1980]
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Different responses elicit different emotions

I failed the test.
I failed the test.

I failed the test.

Oh, again?

You will do better next time!
Thank you.

Yeah…

arousalarousalEmotional impact

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NegativevalenceNurul Lubis

Positivevalence5Neural chat-based dialogue system• RNN encoder-decoder [Vinyals et al., 2015]• Hierarchical recurrent encoder-decoder(HRED) [Serban et al., 2016]• Generating dialogue response with emotional
expression [Zhou et al., 2018]

Application towards emotion elicitationis still very lacking.

13 July 2018

Nurul Lubis[Serban et al., 2016]6

Emotion-sensitive response generation: Emo-HRED[Lubis et al., 2018] in Proc. AAAI 2018

• Encodes emotional context andconsiders it in generating a response• Training data contains responses thatelicit positive emotion• Significant improvement on perceived
emotional impact
Limitations
1. Has not yet learned strategies froman expert
2. Short and generic responses withpositive-affect words
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Challenge and proposal
1. Goal: Learning elicitation strategy
from an expert• Challenge: Absence of data that shows• positive emotion elicitation in everydaysituations• expert strategy in affective dialogue• Proposed: Construct a dialogue corpus
involving an expert in a positiveemotion elicitation scenario

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2. Goal: increase variety in thegenerated response to improveengagement• Challenge: Data sparsity• We hypothesize that higher level
information, e.g. dialogue action, will
reduce data sparsity

Nurul Lubis• categorizing responses• emphasizing this information in thetraining and generation process.

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Proposed architecture:
Multi context HRED (MC-HRED)
A neural dialogue system which generate
response based on multiple dialogue
contexts• Dialogue history• User emotional state• Response action label

MC-HRED architecture.

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Corpus construction
Positive Emotion Elicitation by an Expert

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Data recording design• Goal: learn expert strategy for eliciting positive emotion• Collect:• Interaction between an expert and a participant• Condition the interaction with negative emotion• Expert guides the conversation to allow participant’s emotion recovery and reinstate positiveemotion
Emotionannotation

Briefing
Recorded Session
Opening talk(neutral)

24-Jul-17

Emotion induction
using video(negative)
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Emotion processing
and recovery(positive)
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Data collection and annotation• 60 sessions: 23 hours and 41
minutes of material• 1 counselor, 30 participants• 2 sessions per participant• 1 induced to anger• 1 induced to sadness• Self-report emotion annotation
using Gtrace [Cowie et al., 2000]• Transcription

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Unsupervised Clustering of Counselor Dialogue

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Counselor dialogue clustering Human annotation• Expensive, labor intensive• Low reliability

Goal: To find high-level information• Information equivalent to dialogue acts
• Specific to the dialogue scenario• Retaining affective intents

 Standard dialogue acts classifier• May not cover specific emotion-related
intent in the data

 Unsupervised clustering

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Counselor dialogue clusteringCounselingdata

CounselorCounselorresponses
Counselor
responses
responses

Pre-trained
word2vec
model

K-Means• Need to predefine how manyclusters• We choose K empirically

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VectorizedcounselordialogueK-means
clustering

K-meanslabels

DPGMM
clustering

DPGMM labels

DPGMM• No prior definition of model
complexity

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Analysis
K-means: 8 clusters“So you feel frustrated.”“I guess we all have to be careful.”“Maybe, yes yes.”“Right.”13 July 2018

“Mm mm.”“Yes, hm mm”

K-means: 8 sub-clusters

DPGMM: 13 clusters“Have you thought about thiskind of issue before?”

An assortment ofshorter sentences“So who do you think isresponsible for it?”

An assortment oflonger sentences

Nurul Lubis“Mm mm.”“Yes, hm mm”

16Experiment

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Experimental set-up
Pre-training
Pre-training

SubTleCounselingdata• SubTle corpus [Ameixa et al., 2014] ~5.5M
dialogue pairs from movie subtitle• HRED to retain information acrossdialogue turns

Counselordialogueclustering

Unsupervised
action label

Fine-tuning

Fine-tuning• Counseling corpus• Baseline: Emo-HRED• emotion context• Proposed: MC-HREDTesting• emotion and action contexts• Clust-HRED• Action context
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Pre-training and fine-tuning
Pre-training initializes the weights
of HRED components
Selective fine-tuning: only optimize
parameters affected by newcontexts
MC-HRED is jointly trained on
combined losses• NLL of target response• Emotion prediction error• Action prediction error

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MC-HRED architecture.

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Objective evaluation: perplexity
Model
Emo-HRED
Clust-HRED

MC-HRED

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EmoyesActionPerplexity

no42.60

K-means

39.57

DPGMM

30.57

K-means

29.57

DPGMM

32.04

noyes

• Combining cluster label and emotion
contexts• K-means cluster label shows improvements• DPGMM cluster label slightly worsen

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Objective evaluation: perplexity
Perplexity
Model

EmoAction
all

EmoHRED
ClustHRED

MC-HRED

yes

short

long

no42.60

35.74

61.17

K-means

39.57

32.30

57.37

DPGMM

30.57

24.79

42.25

K-means

29.57

23.23

38.73

DPGMM

32.04

25.00

42.43

noyes

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• Perplexity on short and long queries• Performance on short queries are
consistently better than long ones
• MC-HRED with K-means obtains
substantial improvement on longtriples• The multiple contexts help, especiallyfor long inputs

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Subjective evaluation
4.2
Emo-HRED

4.1

MC-HRED• 100 queries, each judged by 20 crowdworkers

4• Naturalness• Emotional impact• Engagement

3.9
3.8
3.7

3.6
3.5
3.4

13 July 20184.17 4.17

3.79 3.8

3.92 3.99

naturalness

emo_impact

engagement• Improved engagement while
maintaining the emotional impact andnaturalness• MC-HRED produce responses with
2.53 more words on average
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Conclusion
We presented• A corpus showing expert strategy in positive
emotion elicitation• Unsupervised clustering of expert dialogue• A multi-context neural response generation• Improves performance on longer queries• Improves dialogue engagement• Produces longer responses

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Future Work• Multimodal information:
speech, visual• Evaluation through userinteraction23Thank you!

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ExamplesU1

oh how do you feel about that one.

U2

yes i heard the story.

U3 (Target)

you heard it before.

Emo-HREDright.

MC-HRED

it’s a big thing.

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Nurul LubisU1

are you a student here?

U2

uh yes, actually I just got, er that's my lab over there insocial computing yes (laughter).

U3 (Target)

oh really. so you've been watching us going by.

Emo-HREDOh okay.

MC-HRED(laughter) it’s nice to meet you.

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Traditional Works on Emotion
Expression and Recognition

Expression

Emotion expression or simulation• Conveying emotion to user• Increasing closeness and satisfaction[Higashinaka et al., 2008]13 July 2018

Recognition

Emotion recognition• Recognizing user’s emotional state• Increasing task success [Forbes-Riley and Litman,
2012]

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