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.

Nicky: Toward a Virtual Assistant for Test and Measurement Instrument Recommendations

Abstract: Natural language question answering has been an area of active computer science research for decades. Recent advances have led to a new generation of virtual assistants or chatbots, frequently based on semantic modeling of some broadly general domain knowledge. However, answering questions about detailed, highly technical, domain-specific capabilities and attributes remains a difficult and complex problem. In this paper we discuss a prototype conversational virtual assistant designed for choosing test and measurement equipment based on the detailed measurement requirements of the test engineer. Our system allows for multi-stage queries which retain sufficient short-term context to support query refinement as well as compound questions. In addition to the software architecture, we explore an approach to ontology development that leverages inference from reasoners and minimizes the complexity of entering the specifications for a large collection of instruments. Finally, we provide insights into the issues of building this system and provide recommendations for future designs.

A persuasive chatbot using a crowd-sourced argument graph and concerns

Abstract: Chatbots are versatile tools that have the potential of being used for computational persuasion where the chatbot acts as the persuader and the human agent as the persuadee. To allow the user to type his or her arguments, as opposed to selecting them from a menu, the chatbot needs a sufficiently large knowledge base of arguments and counterarguments. And in order to make the user change their current stance on a subject, the chatbot needs a method to select persuasive counterarguments. To address this, we present a chatbot that is equipped with an argument graph and the ability to identify the concerns of the user argument in order to select appropriate counterarguments. We evaluate the bot in a study with participants and show how using our method can make the chatbot more persuasive.

PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning

Abstract: To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning. There are two stages involved in the learning process. In the first stage, a coarse-grained generation model is trained to learn response generation under the simplified framework of one-to-one mapping. In the second stage, a fine-grained generative model augmented with latent variables and an evaluation model are further trained to generate diverse responses and to select the best response, respectively. PLATO-2 was trained on both Chinese and English data, whose effectiveness and superiority are verified through comprehensive evaluations, achieving new state-of-the-art results.

Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review

Abstract: Artificial intelligence (AI) conversational agents (CA) or chatbots represent one of the technologies that can provide automated customer service for companies, a trend encountered in recent years. Chatbot use is beneficial for companies when associated with positive customer experience. The purpose of this paper is to analyze the overall customer experience with customer service chatbots in order to identify the main influencing factors for customer experience with customer service chatbots and to identify the resulting dimensions of customer experience (such as perceptions/attitudes and feelings and also responses and behaviors). The analysis uses the systematic literature review (SLR) method and includes a sample of 40 publications that present empirical studies. The results illustrate that the main influencing factors of customer experience with chatbots are grouped in three categories: chatbot-related, customer-related, and context-related factors, where the chatbot-related factors are further categorized in: functional features of chatbots, system features of chatbots and anthropomorphic features of chatbots. The multitude of factors of customer experience result in either positive or negative perceptions/attitudes and feelings of customers. At the same time, customers respond by manifesting their intentions and/or their behaviors towards either the technology itself (chatbot usage continuation and acceptance of chatbot recommendations) or towards the company (buying and recommending products). According to empirical studies, the most influential factors when using chatbots for customer service are response relevance and problem resolution, which usually result in positive customer satisfaction, increased probability for chatbots usage continuation, product purchases, and product recommendations.