Titlebook: Dialogues with Social Robots; Enablements, Analyse Kristiina Jokinen,Graham Wilcock Book 2017 Springer Science+Business Media Singapore 201

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How to Study a Joseph Conrad Novelignificant human effort. To reduce this human effort, in this paper, we propose an active learning framework to construct example-based dialog systems efficiently. Specifically, we propose two uncertainty sampling strategies for selecting inputs to present to human annotators who create system respo

Intend

How to Study a Joseph Conrad Novelions of the users. It is important that the questions are concise and concrete to prevent users from being annoyed. Our method selects the most appropriate question on the basis of expected utility calculated for four types of question: Yes/No, alternative, 3-choice, and Wh- questions. We define uti

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How to Study a Joseph Conrad Novelerent facilities provided by each. Most of these toolkits include not only a method for representing states and actions, but also a mechanism for reasoning about and selecting the actions, often combined with a technical framework designed to simplify the task of creating end-to-end systems. This ne

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Coping with different kinds of novelnt learning dialogue systems, this system avoids manual feature engineering by performing action selection directly from raw text of the last system and (noisy) user responses. Our initial results, in the restaurant domain, report that it is indeed possible to induce reasonable behaviours with such

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https://doi.org/10.1007/978-1-349-13279-9also enjoy non-task-oriented conversation with the computer. When an IVR system generates a response, it usually refers to just verbal information of the user’s utterance. However, when a person gloomily says “I’m fine,” people will respond not by saying “That’s wonderful” but “Really?” or “Are you

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How to Study: A Practical Guideemains there. In our research, we select the most essential features by applying a self-adaptive multi-objective genetic algorithm. The proposed approach is evaluated using data from different languages (English and German) with two different feature sets consisting of 37 and 384 dimensions, respect

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https://doi.org/10.1007/978-1-349-13279-9uch a corpus is available, neither an agenda nor a statistically-based dialog control logic are options if the domain knowledge is broad. This article presents a module that automatically generates system-turn utterances to guide the user through the dialog. These system-turns are not established be

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https://doi.org/10.1007/978-3-030-45208-7eated by hand. In casual dialogues, the speaker sometimes asks his conversation partner questions about favorites or experiences. Since this behavior also appears in conversational dialogues with a dialogue system, systems must be developed to respond to such questions. However, the effectiveness of