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Errors can occur at every level of a dialogue, from the recognition of what words were spoken to the understanding of the intentions behind the words. Our approach to errorhandling assumes that errors cannot be avoided in spoken dialogue and that it is more useful to focus on methods for detecting and dealing with miscommunication when it occurs. An(More)
In this article we describe how Java can be used to implement an object-based, cross-domain, mixed initiative spoken dialogue manager (DM). We describe how dialogue that crosses between several business domains can be modelled as an inheriting and collaborating suite of objects suitable for implementation in Java. We describe the main features of the Java(More)
Advanced spoken dialogue systems incorporate functionalities such as mixed-initiative and cross-domain dialogues. In this paper an object-based approach to cross domain dialogue modelling is described in which service agents representing primary transaction types and support agents representing tasks such as eliciting payment details are selected as(More)
We present a city navigation and tourist information mobile dialogue app with integrated question-answering (QA) and geographic information system (GIS) modules that helps pedestrian users to navigate in and learn about urban environments. In contrast to existing mobile apps which treat these problems independently, our Android app addresses the problem of(More)
Once a dialogue strategy has been learned for a particular set of conditions, we need to know how well it will perform when deployed in different conditions to those it was specifically trained for, i.e. how robust it is in transfer to different conditions. We first present novel learning results for different ASR noise models combined with different user(More)
We demonstrate a new development environment1 for “Information State Update” dialogue systems which allows non-expert developers to produce complete spoken dialogue systems based only on a Business Process Model (BPM) describing their application (e.g. banking, cinema booking, shopping, restaurant information). The environment includes automatic generation(More)
We demonstrate the REALL-DUDE system1, which is a combination of REALL, an environment for Hierarchical Reinforcement Learning, and DUDE, a development environment for “Information State Update” dialogue systems (Lemon and Liu, 2006) which allows non-expert developers to produce complete spoken dialogue systems based only on a Business Process Model (BPM)(More)