Chatting

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

2. 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?

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. Example dialogue

13. 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

14. 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

15. 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

16. 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

17. 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