Master Thesis MSTR-2018-78

BibliographySchmidt, Maximilian: Neural-based methods for user simulation in dialog systems.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 78 (2018).
65 pages, english.

Spoken Dialog Systems allow users to interact with a Dialog Manager (DM) using natural language, thereby following a goal to fulfill their task. State-of-the-art solutions cast the problem as Markov Decision Process, leveraging Reinforcement Learning (RL) algorithms to find an optimal dialog strategy for the DM. For this purpose, several thousand dialogs need to be seen by the RL agent. A user simulator comes in handy to generate responses on demand, however the current state-of-the-art agenda-based user simulators lack the ability to model real human subjects. In this thesis, this problem is addressed by implementing a user simulator using a Recurrent Neural Network which approximates the agenda-based model in a first step. Going onwards, it is shown to learn noise and variance treated as varying user behavior. This is used to train the simulator on real data thus modeling real users.

Full text and
other links
Department(s)University of Stuttgart, Institute for Natural Language Processing
Superviser(s)Vu, Prof. Ngoc Thang; Schweitzer, Dr. Antje
Entry dateJune 6, 2019
   Publ. Computer Science