By Hamidreza Chinaei, Brahim Chaib-draa
This ebook discusses the partly Observable Markov determination strategy (POMDP) framework utilized in discussion platforms. It provides POMDP as a proper framework to symbolize uncertainty explicitly whereas aiding automatic coverage fixing. The authors suggest and enforce an end-to-end studying strategy for discussion POMDP version elements. ranging from scratch, they current the nation, the transition version, the commentary version after which eventually the gift version from unannotated and noisy dialogues. those altogether shape an important set of contributions that may possibly motivate mammoth extra paintings. This concise manuscript is written in an easy language, filled with illustrative examples, figures, and tables.
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Additional info for Building Dialogue POMDPs from Expert Dialogues: An end-to-end approach
This is called t-step planning. Notice that the number of created beliefs increases exponentially with respect to the planning time t. This problem is called curse of history in POMDPs (Kaelbling et al. 1998; Pineau 2004). Planning is performed in POMDPs as a breadth first search in trees for a finite t, and consequently finite t-step conditional plans. A t-step conditional plan describes a policy with a horizon of t-step further (Williams 2006). It can be represented as a tree that includes a specified root action at .
The HTMM method for intent learning from unannotated dialogs is as follows. 1 Hidden Topic Markov Model for Dialogs Hidden topic Markov model, in short HTMM (Gruber et al. 2007), is an unsupervised topic modeling technique that combines LDA (cf. Sect. 2) and HMM (cf. Sect. 3) to obtain the topics of documents. In Chinaei et al. (2009), we adapted HTMM for dialogs. A dialog set D consists of an arbitrary number of dialogs, d. , the ASR recognition of the actual user utterance u. The recognized user utterance, uQ , is a bag of words, uQ D Œw1 ; : : : ; wn .
In fact, the dialog model aims to provide the dialog manager with better approximates of the environment dynamics. More importantly, the dialog manager is required to learn a strategy based on the updated model and to make a decision that satisfies the user intent during the dialog. But, this is a difficult task primarily because of the noisy ASR output, the NLU difficulties, and also the user intent change during the dialog. Thus, model learning and decision making is a significant task in SDS.
Building Dialogue POMDPs from Expert Dialogues: An end-to-end approach by Hamidreza Chinaei, Brahim Chaib-draa