By G.G. Lee, H.K. Kim, M. Jeong, J.-H. Kim
This ebook covers cutting-edge subject matters at the sensible implementation of Spoken conversation platforms and clever assistants in daily functions. It offers clinical achievements in language processing that lead to the improvement of profitable purposes and addresses normal concerns concerning the advances in Spoken conversation structures with functions in robotics, wisdom entry and conversation. Emphasis is put on the subsequent subject matters: speaker/language acceptance, person modeling / simulation, overview of conversation process, multi-modality / emotion reputation from speech, speech information mining, language source and databases, laptop studying for spoken conversation structures and academic and healthcare applications.
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Additional resources for Natural Language Dialog Systems and Intelligent Assistants
Markov decision processes (MDPs) and POMDPs are the most successful and now widely used to model and train dialogue managers (Roy et al. 2000; Levin et al. 2000; Williams and Young 2007; Young et al. 2010; Yoshino et al. 2013). These approaches allow us to consider all possible future actions of a dialogue system and thus to obtain a new optimal dialogue strategy which could not be anticipated in conventional hand-crafted dialogue systems. The conventional scheme for goal-oriented systems assumes that the task and dialogue goal are clearly defined and readily encoded in the reinforcement learning (RL) reward function.
However, switching a domain or application by accident reduces user satisfaction and task success rate enormously. Therefore, efficient error recovery strategies need to be applied. In an online study, we evaluated three different machine-led error recovery strategies for in-car infotainment systems. They are varied first in terms of modality (visual and speech) and second in using contextual information. By comparing the strategies, we figured out that asking novice users an open question does not work and they prefer to select the domain from a list of alternatives.
The effect on task success is even worse, only around 20 % of the participants reached their goal. In AU there is no significant difference concerning usability. However, task success identified problems in terms of identification of the right application for information retrieval tasks. ” AU and DC strategy may perform better with expert users, but for novice users their success depends on task type and context. 3 Presentation with Different Modalities (Hypothesis 3) As REF and AU do not require to present any visual information to the user, only DLS and DC are compared.