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Probabilistic Adaptive Real-Time Learning And\nNatural Conversational Engine (PARLANCE)
Start date: Nov 1, 2011, End date: Oct 31, 2014 PROJECT  FINISHED 

The project goal is to design and build mobile applications that approach human performance in conversational interaction, specifically in terms of the interactional skills needed to do so. These skills will include recognising and generating conversational speech incrementally in real-time, adapting to new concepts without manual intervention, and personalising interaction. All of these skills will be learned or adapted using real data, and will be used to build systems for interactive hyper-local search in three languages (English, Spanish and Mandarin) and for two domains such as property search and tourist information. Current search engines work well only if the user has a single search goal and does not have multiple trade-offs to explore. For example, standard search works well if you want to know the phone number of a specific business but poorly if you are looking for a house with several different search criteria of varying importance, e.g. number of bedrooms versus bathrooms versus price etc. The latter requires the user to collaborate conversationally over several turns.
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