In this paper we present results on the learning e ects of using the gulan educational system to understand spoken dialogue systems. The investigation is restricted to the dialogue management component, which uses a subset of the linlin dialogue manager. The results are based on an evaluation of questionnaires given to the students and of tutors assessment of the students knowledge before and a...

Semantic processing is vital in a dialogue system for the language understanding stage. Recent approaches of semantic processing rely on machine learning methods to perform the task. These are more robust to errors from the speech recogniser. Although these approaches are built on the domain of the dialogue system they do not incorporate contextual information available in the dialogue system. ...

In this paper, we propose to combine sample-efficient generalization frameworks for RL with a feature selection algorithm for the learning of an optimal spoken dialogue system (SDS) strategy.

In recent years machine learning approaches have been proposed for dialogue management optimization in spoken dialogue systems. It is customary to cast the dialogue management problem into a Markov Decision Process (MDP) and to find the associated optimal policy using Reinforcement Learning (RL) algorithms. Yet, the dialogue state space is usually very large (even infinite) and standard RL algo...

In recent years machine learning approaches have been proposed for dialogue management optimization in spoken dialogue systems. It is customary to cast the dialogue management problem into a Markov Decision Process (MDP) and to find the associated optimal policy using Reinforcement Learning (RL) algorithms. Yet, the dialogue state space is usually very large (even infinite) and standard RL algo...

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across onand offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue usin...

This demonstration will illustrate interactive computer games intended to help a native speaker of English learn Mandarin. These systems provide users with humanlike conversational exercises with contextualized help mechanisms. Two distinctly different activities, a translation game and a dialogue game are illustrated. The level of difficulty can be manipulated, and the sentence variations cove...

Recently, a number of authors have proposed treating dialogue systems as Markov decision processes (MDPs). However, the practical application of MDP algorithms to dialogue systems faces a number of severe technical challenges. We have built a general software tool (RLDS, for Reinforcement Learning for Dialogue Systems) based on the MDP framework, and have applied it to dialogue corpora gathered...

During the last decade, research in the field of Spoken Dialogue Systems (SDS) has experienced increasing growth. However, the design and optimization of SDS is not only about combining speech and language processing systems such as Automatic Speech Recognition (ASR), parsers, Natural Language Generation (NLG), and Text-to-Speech (TTS) synthesis systems. It also requires the development of dial...

Abstract We introduce dGSLM, the first “textless” model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised unit discovery coupled with a dual-tower transformer architecture cross-attention trained 2000 hours two-channel raw conversational (Fisher dataset) without any text or labels. show that our is speech, laughter, and other paralinguistic sig...