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Personalizing a Dialogue System With Transfer Reinforcement Learning

AAAI 2018 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Statistical alignment and hypothesis testing · 100 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a personalized Q-function combining shared dialogue knowledge with user-specific preferencesPETAL · Coffee-shopping test setaverage AUCHelped
Transfer dialogue data from all source users instead of only similar usersCoffee-shopping dialogue system · Coffee-shopping test setaverage AUCHelped
Fit only on target-user data rather than transferring source knowledgeCoffee-shopping dialogue system · Coffee-shopping test setaverage AUCHurt
Use PETAL's personalized policy instead of the compared baselinesPETAL · Simulated coffee-orderingaveraged rewardHelped
Use PETAL's personalized policy instead of the compared baselinesPETAL · Simulated coffee-orderingaveraged success rateHelped
Use PETAL's personalized policy instead of the compared baselinesPETAL · Simulated coffee-orderingaveraged dialogue lengthHelped

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