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On-line dialogue policy optimisation Milica Gašić Dialogue Systems Group

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Spoken Dialogue System Optimisation Problem: What is the optimal behaviour Solution: Find it automatically through interaction

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Reinforcement learning

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Training in interaction with humans Problem 1: Optimisation requires too many dialogues Problem 2: Training makes random moves Problem 3: Humans give inconsistent ratings

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Outline Background Dialogue model Dialogue optimisation Sample-efficient optimisation Models for learning Robust reward function Human experiments Conclusion

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Model: Partially Observable Markov Decision Process atat stst s t+1 rtrt otot o t+1 State is Markov -- depends on the previous state and action: P(s t+1 |s t, a t ) – the transition probability State is unobservable and generates a noisy observation P(o t |s t ) -- the observation probability In every state action is taken and a reward is obtained Dialogue is a sequence of states Action selection (policy) is based on the distribution over all states at every time step t – belief state b(s t )

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Dialogue state factorisation Decompose the state into conditionally independent elements: user goal user action stst gtgt utut dtdt dialogue history atat rtrt otot o t+1 g t+1 u t+1 d t+1

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Further dialogue state factorisation gtgt utut dtdt atat rtrt otot o t+1 g t+1 u t+1 d t+1 g t food d t food u t food g t area d t area u t area g t+1 food d t+1 food u t+1 food g t+1 area d t+1 area u t+1 food

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Policy optimisation in summary space Compress the belief state into a summary space 1 J. Williams and S. Young (2005). "Scaling up POMDPs for Dialogue Management: The Summary POMDP Method." Original Belief Space Actions Policy Summary Space Summary Actions Summary Function Master Function Summary Policy

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Q-function Q-function measures the expected discounted reward that can be obtained at a summary point when an action is taken Takes into account the reward of the future actions Optimising the Q-function is equivalent to optimising the policy Discount factor in (0,1] Reward Starting summary point Starting action Expectation with respect to policy π

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Online learning Reinforcement learning in direct interaction with the environment Actions are taken e-greedily Exploitation: choose action according to the best estimate of Q function Exploration: choose action randomly (with probability e) In practice 10,000s of dialogues are needed!

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Problem 1: Standard models require too many dialogues

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Solution: Take into account similarities between different belief states Essential ingredients Gaussian process Kernel function Outcome Sample-efficient policy optimisation

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Gaussian Process Policy Optimisation The Q-function is the expected long-term reward It can be modelled as a Gaussian process Prior: Posterior, given visited summary states, actions and obtained rewards:

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Voice mail example Voice mail example: The user asks the system to save or delete the message. The user input is corrupted with noise, so the true dialogue state is unknown. belief state b(s)

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The role of kernel function in a Gaussian Process The kernel function models correlation between different Q-function values Confirm Q-function value Action Belief state Confirm

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Problem 2: Standard models make random moves Exploitation? Exploration?

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Solution: Define a stochastic policy Gaussian process defines Gaussian distributions for each action Sample from these distributions Automatically deal with exploration/exploitation Outcome: Less unexpected behaviour

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Results during testing (with simulated user)

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Results during training (with simulated user)

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Problem 3: Humans give inconsistent ratings Reward is a measure of how good the dialogue is

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On-line learning from user rating

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User rating inconsistency Random policyOnline learned policy Simulator trained policy User rating (%) Objective score (%) P(user rating=1|objective score=1) P(user rating=1| objective score=0)

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Solution: Incorporate both objective and subjective evaluation

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Evaluation results Simulator trainedOn-line trained Evaluation dialogues Reward11.6 +/ /- 0.3 Success (%)93.5 +/ /- 0.9

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Conclusions GP in policy optimisation Automate dialogue manager optimisation Enable sample efficient optimisation Outperforms simulator trained policies

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