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OpenDial Framework Svetlana Stoyanchev SDS seminar 3/23

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OpenDial modules Rule-structured components – Collection of probabilistic rules with a trigger variable External components: monitor dialog state and update it when relevant changes are detected Seminar on Spoken Dialogue Systems, Columbia 2015 2

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Open Dial Defines dialogue in terms of utterances and dialogue acts – U_u - user utterance – U_m – machine utterance – A_u – user dialog act – A_m – machine dialog act Define dialogue rules using ‘triggers’ and ‘effects’ (if-then-else) using XML representation Allows probabilistic rules Seminar on Spoken Dialogue Systems, Columbia 2015 3

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Formalization Represent distribution with the help of if...then...else construction – Condition on particular state variables – Distribution over possible effects

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Probabilistic rules The DM applies probability/utility rules What is the difference between probability & utility? – Probability rules - express probability over effects – Utility rules – express utility distribution Seminar on Spoken Dialogue Systems, Columbia 2015 5

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Probability rules Form: If … then … else … condition effect Seminar on Spoken Dialogue Systems, Columbia 2015 6

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Probability rules Conditions can be arbitrarily complex logical formulae Effect: define probability (of each possible effect) cumulative effect of a condition sum to 1

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Utility rules Assign utility values to particular system decisions Same form as probability rules BUT Probabilistic effects are replaced by utility distribution

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Utility rule

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When are utility rules useful? Utility – most utility rules only include one single decision variable – the possibility to integrate multiple decision variables where the system can execute multiple actions in parallel communicate through both verbal and non-verbal channels – Dealing with uncertainty of ASR/NLU

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Example Rules y is a direction (left, right, up, down, forward, backward) If system’s previous DA was AskRepeat, there is 90% chance that the user will repeat

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Example

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Example (1) Seminar on Spoken Dialogue Systems, Columbia 2015 13.4 =.6*2 -.4*.2 -.4 = -.6*2 +.4*.2.5 (defined by r10, i.e. threshold) Prob. Dist. For next DA.54 =.6*.9.36 =.4*.9 Best Action: AskRepeat

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Example (2) Seminar on Spoken Dialogue Systems, Columbia 2015 14 Combining predicted and actual user response

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Framework allows DM to prime the recognition hypotheses of the user’s DA based on previous DAs Utilities and Probabilities can be learned from data

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Questions How to plug in a probabilistic NLU, trained on data? e.g. to understand all cities in a database? Seminar on Spoken Dialogue Systems, Columbia 2015 16

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