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A chatbot is an example of cognitive computing system that emulates human conversations to provide informational, transactional, and conversational services. Despite their widespread adoption, chatbots still suffer from a number of performance issue due to limitations with their programming and training. In this paper we discuss Human Aided Chatbots, i.e. chatbots that rely on humans in the loop to operate. Human Aided Chatbots exploit human intelligence, brought for instance by crowd workers or full-time employees, to fill the gaps caused by limitations of fully automated solutions. A recent example of Human Aided Chatbots is Facebook M. To achieve broader adoption, Human Aided Chatbots must overcome a number of issues, including scalability, low-latency, and privacy. In this short paper, we discuss how Crowd Computing performed in the enterprise could help overcoming such issues. We present some recent findings in the field of Enterprise Crowd Computing, and introduce ECrowd, a platform for enterprise crowd computing designed for gathering training data for cognitive systems.
Alessandro Bozzon (Mon,) studied this question.