Design and First Tests
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Design and First Tests of a Chatter Hans Dybkjær SpeechLogic ™ , Prolog Development Center A/S & Laila Dybkjær NISLab, University of Southern Denmark. Chatting. Dialogue type not common in state-of-the-art Eliza, chatbots: written interaction New kinds of application edutainment

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Chatting

Design and First Tests of a ChatterHans Dybkjær SpeechLogic™, Prolog Development Center A/S&Laila DybkjærNISLab, University of Southern Denmark


Chatting

Chatting

  • Dialogue type not common in state-of-the-art

    • Eliza, chatbots: written interaction

  • New kinds of application

    • edutainment

    • chat with character from commercials series

    • small-talk while waiting instead of music

  • Test-bed for new conversational techniques

    • express feelings

    • understand feelings

    • non-task oriented dialogue

    • other new features

How far can we push current technology towards free conversation?


Chatting

Kurt

  • Entertain users through chat (in Danish)

  • Limited vocabulary (350 words)

  • Phone-based

  • Preferences of food, notably fruit and vegetables

  • Kurt, e.g. his name, his age, and where he works

  • Personality

    • childish

    • affective

    • self-centred

    • defensive with an underlying uncertainty

    • evasive

Personality designed to hide shortcomings of understanding level


Features for emotion modelling

Available

(Phonetic) lexicon

Grammar

Recognition scores

Phrasing

Dialogue flow

Available, but not used:

n-best ambiguity

barge-in event handling

complex task domain

Not available (input)

Glottal stop

Stress

Prosody

Non-linguistic vocal phenomena, e.g. laughter

Mood (anger, joy, ...)

Aware sites

Overlapping speech(back-channelling)

...

Features for emotion modelling

Platform allows limited emotion modelling features


Interaction model

Interaction model

You are stupid

Fool yourself, …

Linguistic

personality

Compute

affect

Generate

output

s t a t e

Flow

model

Manage

dialogue

Standard dialogue model extended with affective state and handling


Linguistic personality

Linguistic personality

Lexicon tagged with

  • Face value

  • Preference

  • Embarrassment

    Used for

  • Input interpretation

Face value

  • Kurt sensitive to losing face

  • Negative face value: e.g. corrections and insults

  • Positive face value: e.g. praise

    Preference

  • Words are liked, disliked or neutral

    Embarrassment

  • Certain words embarrassing

  • All other words neutral

stupid

Fool, …

pers’lity

affect

output

s t a t e

flow

manage

Context-independent assumption


Negation

Negation

  • Changes face value and preference

  • Does not affect embarrassment

  • Syntactic negation:

    • you are not stupid

  • Semantic negation:

    • you hate apples

  • Implication of negation may depend on question or statement

    • you hate apples = don’t you hate apples

    • you are not stupid ≠ aren’t you stupid

      Though = and ≠ are not fully semantically correct, they holdwith respect to face value and preference

More complex logic negation not useful for spoken language


Affect computation

Affect computation

Self-confidence

  • Recognition scores

  • Changed by accept/reject

    Embarrassment

  • Means topic change

Face value

  • Complex, simplify:

    • if any negative input, take minimum

    • otherwise take maximum

      Preference

  • Positive/negative face value => knock-on effect

  • Not a function of single words

  • But:

    • if any negative input, take minimum

    • otherwise take maximum

stupid

Fool, …

pers’lity

affect

output

s t a t e

flow

manage

Simplified but transparent


Affective state

Affective state

Self-confidence

  • Influences

    • magnitude of satisfaction changes

    • flow

Satisfaction

  • Main personality control

    • scale from angry (low) to exalted (high)

  • Overflow at both ends

  • Initial level is neutral

  • Changes computed from

    • input preference

    • input face value

    • self-confidence level

stupid

Fool, …

pers’lity

affect

output

Hangup

Get Angry

s t a t e

flow

Angry

Current

Exalted

manage

Two-parameter model


Dialogue management

Dialogue management

Flow model

  • Questions

  • Answers

  • Statements

  • Jokes

  • Feedback

    • implicit, explicit

Embarrassment

  • Joke and change topic

    Satisfaction

  • ”Underflow” leads to hangup

  • No other flow effect

    Self-confidence

0

low

medium

high

1

Feedback:

Explicit

Implicit

None

stupid

Fool, …

pers’lity

At accept:

Joke

Joke

None

affect

output

s t a t e

flow

manage

Simple task solving plus some more chat-like interaction


Generate output

Generate output

Phrases

  • Canned

  • Composed of:

    • Change marker

    • Insults and jokes

    • Answers and feedback

    • Prompts

Change marker

  • Notifies user of system’s emotional state

  • Function of satisfaction state and satisfaction change

    • High, high: Happy

    • Low, low: Angry

    • High, low: Forbearing

    • Low, High: Distrustful

      Random phrases

  • Variation, less rigid

stupid

Fool, …

pers’lity

affect

output

s t a t e

flow

manage

A simple scheme with large variability


Example dialogue

Example dialogue


Data collection

Data collection

  • No controlled experiments

  • Dialogues collected from demo-line

  • 86 dialogues transcribed from 3 system iterations

  • Many dialogues performed by children

  • First output voice by 40 years old male

  • Second output voice by 14 years old boy

Small but sufficient to give impression


Learned from dialogues 1

Learned from dialogues (1)

  • Start

    • identity

    • age

    • location

    • knows about

    • how are you

  • During call

    • mostly questions concerning Kurt

    • maybe search for common ground

    • little volunteered information

    • dialogue on the conversation

Dinner party conversation with a twist


Learned from dialogues 2

Learned from dialogues (2)

  • Topics asked about by users

    • personal (where he works, where he lives, childhood, wife, children, health, hair, eye-colour, glasses, smokes, …) (parents, …)

    • adjective descriptions (stupid, clever, handsome, …)

    • likes and dislikes (alcohol, food, football, music, work, sex, …)

    • utterances related to what the system says (insults, long input, …)

Topics depends on modelled person


Next steps

Next steps

  • Extend grammar coverage

  • Extend Kurt’s knowledge about himself

  • Provide him with interests

  • Let Kurt ask questions about the user

  • Experiment with addition of new parameters (patience, balance, self-esteem, pessimism/optimism)

  • Weighting of parameters depends on personality

  • New kinds of interaction patterns (hand over phone, detection of repeated calls from same number)

Extended conversational and emotional coverage


Conclusion

Conclusion

  • Clearly too small vocabulary and grammar for longer interactions

  • Entertaining despite all shortcomings

  • In particular

    • repetition of what was understood

    • reactions to insults

Simple but entertaining aspects


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