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Applying spoken dialogue systems (SDS's) is growing in the real life more rapidly because of the advances in the design and management of these systems. The traditional touch tone computer telephony systems are being substituted by the SDS's. In a typical SDS, the user speaks naturally to the system through a phone line and the system provides the required information or performs the required action. Banking and ticket reservation are typical examples of the prevalent SDS's. A spoken dialogue system has four units: automatic speech recognition (ASR), natural language understanding (NLU), dialogue management (DM), and spoken language generation (SLG). In this work, the first spoken dialogue... 

The use of Spoken Dialogue Systems is growing everyday and these systems will substitute current Iterative Voice Response systems in near future. A Spoken Dialogue System consists of Speech Recognition, Language Understanding, Dialogue Management, Speech Generation and Text to Speech Modules. Among these modules the only one that is specific part of Dialogue Systems is Dialogue Management. The responsibility of this part is to determine system behavior to maximize specific variables such as user goal finding accuracy and speed of finding the goal. There were different approaches to dialogue management in recent years the use of Partially-Observable Markov Decision Processes was very popular... 

In contrast to automatic speech recognition (ASR), which converts a speaker’s spoken utterance into a text string, spoken language understanding (SLU) is aimed at interpreting user’s intentions from their speech utterances. Traditionally, this has been accomplished by writing context-free grammars (CFGs) or unification grammars (UGs) manually. The manual grammar authoring process is laborious and expensive, requiring much expertise. In addition, robustness is a vital requirement of these modules, because the input of these modules comes from a speech recognition unit and always contains errors. In recent years, many data-driven models have been proposed for spoken language understanding, but... 

Conversation modeling is one of the most important goals in the field of understanding natural language and machine intelligence. Recently, with the enormous growth of the Internet and social networks, the amount of available data on the Web has increased significantly.This makes it possible to use data-driven approaches to solve the modeling problem of conversation.One of the most recent data-driven methods is the sequence to sequence modeling. In this document, after providing the necessary prerequisites, we examined the various models that have used the sequence to sequence approach for conversation modeling. We further examined the ways of improving the efficiency of this modeling... 

Despite significant advances in dialog systems, data-driven dialog systems are often unable to have content-driven conversations and present real-world knowledge in the context which is due to the lack of knowledge-based conversations in the research datasets and the lack of external knowledge in their architecture. As a result, they are far from the real world and opendomain use-cases. The goal of this research is to introduce a dialogue system based on external knowledge and facts using Deep Learning that the external knowledge can be updated and, the model will adapt itself and take them into account to have a rich conversation. It must be noted that external knowledge is assumed as a...