Affect-sensitive Dialogue Response
Generation for Positive Emotion Elicitation
Nurul Fithria Lubis
Augmented Human Communication (AHC) Lab

Nara Institute of Science and Technology (NAIST)

Affective dialogue systems

High potential of dialoguesystems to address theemotional needs of users• Increase of dialogue system works
and applications 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]

15 March 2019

Nurul Lubis2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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Research goal:
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

UserSystem
User

I failed the test.
I failed the test.
Oh, again?

I failed the test.
You will do better next time!

Yeah…

Thank you.
arousalarousalEmotional impact

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NegativevalenceNurul Lubis

Positivevalence5Positive emotion elicitation does NOT mean
always responding with positive emotion
How was your day?

There are situations where“happy responses” can leadto negative impact

Terrible. Work did not go well.
I’m glad to hear that!

Expressing negative
emotion can lead topositive impact

How was your day?

• System should learn theproper strategy

Terrible. Work did not go well.
That’s too bad.

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Nurul Lubis6

Neural chat-based dialogue system• End-to-end modeling of chat dialogue• 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]

Not yet an application towards
emotion elicitation

15 March 2019[Serban et al., 2016]

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Proposed approach

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Emotion-sensitive response generation: Emo-HRED

Encodes emotional context andconsiders it in generating a response
Train on responses that elicit positiveemotion

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Training Emo-HREDOptimization• Train on combined losses• Negative Log Likelihood (NLL) of target
response• Emotion prediction error• The emotion encoder targets theemotion label of the dialogue turn• The final cost is used to optimize theentire network

Pre-training and selective fine-tuning• Emotion-annotated data is limited• Start by pre-training HRED with largescale conversational data
• Learning semantic and syntactic
knowledge• Selectively fine-tune Emo-HRED withthe emotion-annotated data
• Only train parts that are affected byemotion context• Avoid over-fitting or destabilizing• Adam optimizer

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Datasets
Existing data

SubTle Database

Positive-emotion eliciting data
SEMAINE
Database

default
datasetdialoguesystemsystemresponse• For pre-training• Large-scale conversational corpus
from movie subtitles (5.5M triples)
SEMAINE Database• Small amount of conversation
between user and listening agent inWoZ fashion (2K triples)humanjudgement

positiveemotion eliciting
datasetSEMAINE-positive• For fine-tuning• Augmenting an existing corpus• Contains positive-emotion eliciting
responses
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Evaluation

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Objective evaluation: Perplexity
Pre-training: SubTle
Fine-tuning: SEMAINE-positive
Testing: SEMAINE-positive
ModelBaseline HRED
Proposed Emo-HRED

Parameter
updatestandard
selective
selective

Perplexity
on SEMAINEpositive test set

121.44100.94
42.26

Emotion information can be leveraged in response generation
to reduce perplexity

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Subjective evaluation
3.5• Evaluation via crowdsourcing

3.4• 100 test queries, 20 judgments each• Naturalness• Positive emotional impact

3.3

Likert score• Likert scale 1 to 5 (higher is better)

The proposed model is perceived as
more natural and significantly elicits amore positive emotion (𝑝 < 0.05)

3.2

3.1
3

3.26 3.27
naturalness

HRED positive

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3.22 3.39
positive emotional
impactEmo-HRED positive

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Conclusion

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15Conclusion• We proposed: a dialogue response generator to elicit positive emotion• Considers emotional context of dialogue• Trained on constructed corpus that contains responses with positive emotional impact• Subjective and objective evaluation show improvement over system that ignores
emotion information• More natural• Elicit a more positive emotion impact• Future work
• Collect and utilize more emotion rich dialogue data
• Richer dialogue context• Other modalities• Longer context

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Thank you

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Automatically retrieve responses with positive impact
by utilizing example-based dialogue system approach(Lubis et al., 2017) in Proc. IWSDS 2017
ExampleDatabase

Query

Traditional EBDM

text

Semantic
similarity
scoring
best

emotion10-bestEmotioncorrelation scoringemotional change

3-bestProposed EBDM

Response

25-Mar-19

Emotional
impactscoring
best

Response

Nurul Lubis• Semantic similarity:
text cosine similarity between
query and example query• Emotion correlation: valence& arousal Pearson’s score
between query and example
query• Emotional impact: valencechange in the example triple
Evaluation shows that theproposed EBDM is perceived as
more natural and elicit a morepositive impact

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