Hatim Khouzaimi

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This paper describes a French Spoken Dialogue System (SDS) named NASTIA (Negotiating Appointment SeTting InterfAce). Appointment scheduling is a hybrid task halfway between slot-filling and negotiation. NASTIA implements three different negotiation strategies. These strategies were tested on 1734 dialogues with 385 users who interacted at most 5 times with(More)
Incrementality as a way of managing the interactions between a dialogue system and its users has been shown to have concrete advantages over the traditional turn-taking frame. Incremental systems are more reactive, more human-like, offer a better user experience and allow the user to correct errors faster, hence avoiding desynchronisations. Several(More)
In this paper, reinforcement learning (RL) is used to learn an efficient turn-taking management model in a simulated slot-filling task with the objective of minimis-ing the dialogue duration and maximising the completion task ratio. Turn-taking decisions are handled in a separate new module , the Scheduler. Unlike most dialogue systems, a dialogue turn is(More)
In this paper, a turn-taking phenomenon taxonomy is introduced, organised according to the level of information conveyed. It is aimed to provide a better grasp of the behaviours used by humans while talking to each other, so that they can be methodically replicated in spoken dialogue systems. Five interesting phenomena have been implemented in a simulated(More)
Vers une approche simplifiée pour introduire le caractère incrémental dans les systèmes de dialogue Abstract. Incremental dialogue is at the heart of current research in the field of dialogue systems. Several archi-tectures and models have been published such as (Allen et al., 2001; Schlangen & Skantze, 2011). This work has made it possible to understand(More)
In this article, reinforcement learning is used to learn an optimal turn-taking strategy for vocal human-machine dialogue. The Orange Labs' Ma-jordomo dialogue system, which allows the users to have conversations within a smart home, has been upgraded to an incremental version. First, a user simulator is built in order to generate a dialogue corpus which(More)
The automatic prediction of the quality of a dialogue is useful to keep track of a spoken dialogue system's performance and, if necessary, adapt its behaviour. Classifiers and regression models have been suggested to make this prediction. The parameters of these models are learnt from a corpus of dialogues evaluated by users or experts. In this paper, we(More)