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Do you know of any simple (form filling) dialogue state tracking systems with a graphical model based dialogue state representation? I'm looking for one to get familiar with the overall model and especially data driven learning/inference procedure.

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This Theano tutorial provide a dialogue state tracking systems (slot filling) with a graphical model based (recurrent neural networks) dialogue state representation.

You probably need a decently large training set though: on the Fourth Dialog State Tracking Challenge (DSTC4) last year, we (and other teams) unsuccessfully tried some neural networks but in the end a simple classifier with decent features beats them. (more details: Franck Dernoncourt, Ji Young Lee, Trung H. Bui, and Hung H. Bui. "Robust Dialog State Tracking for Large Ontologies". International Workshop on Spoken Dialogue Systems. 2016.)

  • Thanks, looks interesting! Although in my opinion, a very important part in dialogue state tracking is filling out a form of slots via sequential turn taking (action selection part being out of the scope) while keeping track of the overall progress. Does this model naturally extend to such problem? – Igor Shalyminov Mar 24 '16 at 21:35
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These are some examples I've found myself:

https://github.com/jeremyfix/dstc (rule-based, advanced visualization)

https://github.com/CallumMain/DNN-DST (Deep Neural Net-based)

https://github.com/UFAL-DSG/xtrack2 (Recurrent Neural Net-based)

https://github.com/UFAL-DSG/alex/blob/master/alex/components/dm/dstc_tracker.py (Bayesian Discriminative tracking)

They all are built around the Dialog State Tracking Challenge, and while most of them are not exactly PGM-based, they do track dialogue progress along with turn-wise slot filling.

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