8,972 research outputs found
Survey on Chatbot Design Techniques in Speech Conversation Systems
Human-Computer Speech is gaining momentum as a technique of computer interaction. There has been a recent upsurge in speech based search engines and assistants such as Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like responses. This type of programme is called a Chatbot, which is the focus of this study. This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the last decade. The paper discusses the similarities and differences in the techniques and examines in particular the Loebner prize-winning Chatbots
May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability
Research in explainable AI (XAI) aims to provide insights into the
decision-making process of opaque AI models. To date, most XAI methods offer
one-off and static explanations, which cannot cater to the diverse backgrounds
and understanding levels of users. With this paper, we investigate if free-form
conversations can enhance users' comprehension of static explanations, improve
acceptance and trust in the explanation methods, and facilitate human-AI
collaboration. Participants are presented with static explanations, followed by
a conversation with a human expert regarding the explanations. We measure the
effect of the conversation on participants' ability to choose, from three
machine learning models, the most accurate one based on explanations and their
self-reported comprehension, acceptance, and trust. Empirical results show that
conversations significantly improve comprehension, acceptance, trust, and
collaboration. Our findings highlight the importance of customized model
explanations in the format of free-form conversations and provide insights for
the future design of conversational explanations
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