Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

Medical Chatbot for Novel COVID-19

Abstract: Conversational agents or more universally known as the chatbot were industrialized to respond to user’s queries in a particular domain. Chatbot would serve as a software delegate which enables a computer to converse with human via natural language. A chatbot is a human-like conversational character (Shaikh et al. Int J Eng Sci Comput 6:3117–3119, 2016 [1]. This technology was coined in 1960s, with the intention to impersonate a human (how he would reply to a particular situation) so that the user feels that he is talking to a real person and not a machine. Conversational agent that interacts with user’s turn by turn using natural language (Shawar A, Atwell E (2005) ICAME J Int Comput Arch Mod Med English J 29, 5–24, 2005 [2]). The world of chatbot has seen much of the advance since the invention, and they have progressed from conventional rule-based chatbot to unorthodox AI-based chatbot. The chat agents are expert in their fields [3]. The prime focus of this paper is to show implementation of a retrieval-based chabot with voice support, and we will investigate other standing chatbot and how it is useful in helping the patients fetching all the necessary details about COVID-19.

Method and apparatus for providing chatbot platform

Abstract: Disclosed are a method for providing a chatbot platform and a device thereof The method for providing a chatbot platform comprises the following steps of: enabling a chatbot platform service server to interconnect with a plurality of chatbot servers; enabling the chatbot platform service server to receive request information from a user device; and enabling the chatbot platform service server to select a selecting chatbot server among the plurality of chatbot servers based on the request information

Supporting Creation of FAQ Dataset for E-Learning Chatbot

Abstract: Recently, many universities provide e-learning systems for supporting classes. Though the system is an effective and efficient learning environment, it usually lacks a dynamic user support systems. A chatbot is a good choice to support a dynamic QA however, it is difficult to collect the large number of Q&A data or high-quality datasets required to train the chatbot model to obtain high accuracy. In this paper, we propose a novel framework for supporting dataset creation. This framework provides two recommendation algorithms: creating new questions and aggregating semantically similar answers. We evaluated our framework and confirmed that the framework can improve the quality of an FAQ dataset.

#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment.

Abstract: Inspired by the recent social movement of #MeToo, we are building a chatbot to assist survivors of sexual harassment cases (designed for the city of Maastricht but can easily be extended). The motivation behind this work is twofold: properly assist survivors of such events by directing them to appropriate institutions that can offer them help and increase the incident documentation so as to gather more data about harassment cases which are currently under reported. We break down the problem into three data science/machine learning components: harassment type identification (treated as a classification problem), spatio-temporal information extraction (treated as Named Entity Recognition problem) and dialogue with the users (treated as a slot-filling based chatbot). We are able to achieve a success rate of more than 98% for the identification of a harassment-or-not case and around 80% for the specific type harassment identification. Locations and dates are identified with more than 90% accuracy and time occurrences prove more challenging with almost 80%. Finally, initial validation of the chatbot shows great potential for the further development and deployment of such a beneficial for the whole society tool.

Response Quality in Human-Chatbot Collaborative Systems

Abstract: We report the results of a crowdsourcing user study for evaluating the effectiveness of human-chatbot collaborative conversation systems, which aim to extend the ability of a human user to answer another person's requests in a conversation using a chatbot. We examine the quality of responses from two collaborative systems and compare them with human-only and chatbot-only settings. Our two systems both allow users to formulate responses based on a chatbot's top-ranked results as suggestions. But they encourage the synthesis of human and AI outputs to a different extent. Experimental results show that both systems significantly improved the informativeness of messages and reduced user effort compared with a human-only baseline while sacrificing the fluency and humanlikeness of the responses. Compared with a chatbot-only baseline, the collaborative systems provided comparably informative but more fluent and human-like messages.