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.

Conversational Agent Research Toolkit: An alternative for creating and managing chatbots for experimental research1

Abstract: Conversational agents in the form of chatbots available in messaging platforms, or smartphone and home-based virtual assistants, are gaining increasing relevance in our communication environment. Based on natural language processing and generation techniques, they are built to automatically interact with users in several contexts. We present here a tool, the Conversational Agent Research Toolkit (CART), aimed at enabling researchers to create conversational agents for experimental studies using computational methods. CART is an alternative to Wizard of Oz studies, in which researchers usually resort to (human) research assistants to pose as automated conversational agents. This paper provides an overview of the tool, discusses the role of computational methods in enabling research with conversational agents, and provides a step-by- step tutorial of to design an experiment with a chatbot.

Recent advances in conversational NLP : Towards the standardization of Chatbot building.

Abstract: Dialogue systems have become recently essential in our life. Their use is getting more and more fluid and easy throughout the time. This boils down to the improvements made in NLP and AI fields. In this paper, we try to provide an overview to the current state of the art of dialogue systems, their categories and the different approaches to build them. We end up with a discussion that compares all the techniques and analyzes the strengths and weaknesses of each. Finally, we present an opinion piece suggesting to orientate the research towards the standardization of dialogue systems building.

An Intelligent Behaviour Shown by Chatbot System

Abstract: Chatbots are software agents used to interact between a computer and a human in natural language. Just as people use language for human communication, chatbots use natural language to communicate with human users. The main aim of their creation was to resemble a human being in the way they perform said interaction, trying to make user think that they are writing to a human. In this paper, we analyse some existing chatbot systems namely ELIZA and ALICE and then concludes that it is easier to build bots using ALICE because of its simple pattern matching techniques that building one for ELIZA since it is based on rules. Finally, we discuss our proposed system. In particular, the proposed system is the implementation of ALICE chatbot system as a domain specific chatterbox which is a student information system that helps users in various queries related to students and universities.

Personas Design for Conversational Systems in Education

Abstract: This research aims to explore how to enhance student engagement in higher education institutions (HEIs) while using a novel conversational system (chatbots). The principal research methodology for this study is design science research (DSR), which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This paper focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, specifically the K-means clustering technique. Data analysis is conducted using two datasets. Three methods are used to find the K-values: the elbow, gap statistic, and silhouette methods. Subsequently, the silhouette coefficient is used to find the optimal value of K. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically K-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations. It will cover building SEFMs, building tailored interaction models for these personas and then evaluating them using chatbot technology.