Building Dialogue POMDPs from Expert Dialogues: An by Hamidreza Chinaei, Brahim Chaib-draa

By Hamidreza Chinaei, Brahim Chaib-draa

This e-book discusses the in part Observable Markov choice method (POMDP) framework utilized in discussion platforms. It provides POMDP as a proper framework to symbolize uncertainty explicitly whereas assisting computerized coverage fixing. The authors suggest and enforce an end-to-end studying strategy for discussion POMDP version parts. ranging from scratch, they current the nation, the transition version, the remark version after which eventually the present version from unannotated and noisy dialogues. those altogether shape an important set of contributions which may very likely encourage huge additional paintings. This concise manuscript is written in an easy language, packed with illustrative examples, figures, and tables.

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B/. b; a; o0 /, is calculated from Eq. 3). Notice that we can see a POMDP as an MDP, if the POMDP includes a deterministic observation model and a deterministic initial belief. 1 The process of policy learning in the Q-learning algorithm (Schatzmann et al. 2006) 27 a1 a2 a3 ... 77 ... 39 ... 01 ... 58 ... 93 ... ::: ... ... in Eq. 3), by starting with a deterministic initial belief, the next belief will be deterministic as the observation model is deterministic. This means that such a POMDP knows its current state with 100 % probability similar to MDPs.

The algorithm then iterates on the two steps of policy evaluation and policy improvement. , Line 7, the algorithm calculates the value of policy tC1 . This is done efficiently by calculating the value of VkC1 using the value function Vk of previous policy t , and then repeating this calculation until it finds a converged value for Vk . s0 /: s0 2S The algorithm iterates until for all states s the state values stabilize. s/j < , where is a predefined threshold for error. , Line 10, the greedy policy tC1 is chosen.

In this section, we go through the second step of our descriptive Algorithm 1: extracting actions directly from dialogs and learning a maximum likelihood transition model. In Sect. 3 Learned probabilities of intents for the recognized utterances in the SACTI-1 example uQ 1 ... 00 ... the state s1 . 4) To do so, we extract the set of possible actions from the dialog set. Then, the maximum probable intent (state) is assigned to each recognized utterance using Eq. 3). For instance, for the recognized utterances in the SACTI-1 example, we can learn the probability distribution of the intents from Eq.

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