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.

Model of Multi-turn Dialogue in Emotional Chatbot

Abstract: The intent recognition and natural language understanding of multi-turn dialogue is key for the commercialization of chatbots. Chatbots are mainly used for the processing of specific tasks, and can introduce products to customers or solve related problems, thus saving human resources. Text sentiment recognition enables a chatbot to know the user’s emotional state and select the best response, which is important in medical care. In this study, we combined the multi-turn dialogue model and sentiment recognition model to develop a chatbot, that is designed for used in daily conversations rather than for specific tasks. Thus, the chatbot has the ability to provide the robot’s emotions as feedback while talking with a user. Moreover, it can exhibit different emotional reactions based on the content of the user’s conversation.

Chatbot application in a 5th grade science course.

Abstract: This study aims to investigate the effect of chatbots that work with artificial intelligence on the success of students and their opinions about chatbots in the ‘Matter and the changing state of matter’ unit in the 5th grade science course. In addition to text-based functions, the designed chatbot includes a video accessed on the web to support students visually and aurally. The chatbot was designed using the Dialogflow program and an instant messaging program made available to students through a group created on Telegram. The study, which used a quasi-experimental pretest-posttest design, included 41 participants (n = 20 for the experimental, n = 21 for the control group) studying in the 5th grade of a state secondary school in the 2020-2021 academic year. Results suggest that although there was no significant difference between the experimental and control groups in terms of academic achievement, it was determined that the chatbot application positively affected the online learning experience of the experimental group students. Students’ opinions about the chatbot included that it was useful and fun, they would like to use it for other courses, it provided useful assistance in learning outside the classroom, and it allowed them to repeat the course again. The results showed that, especially during the Covid-19 pandemic, such applications could contribute positively to students’ learning.

Jollity Chatbot- A contextual AI Assistant

Abstract: Chatbot is a software application that can stimulate a conversation via text, instead of direct contact with a live human through messaging applications, websites and mobile applications. Chatbot applications help to make interactions between people and services by enhancing the customer experience. Chatbot is widely used in the areas of food ordering, ecommerce and transportation, etc. Practically it is not possible to find a permanent companion to make us happy all the time. Hence, this paper has planned to design a jollity chatbot to talk with the human users and make sure that it entertains and give suggestion and motivation in tough times. The jollity chatbot is implemented in Rasa, an open -source conversational AI framework and it is easy to customize. The proposed method has added 12 intents with each more than 8 text examples constituting a total of 100 input samples in nlu.md and their response in domain.yml. The flow of interactions is given in stories.md. The jollity chatbot is deployed in Telegram using ngrok and the server URL details and the access token are given in the credentials.yml. The system is experimented with various evaluation measures like accuracy of the intents, accuracy of the stories and the confusion matrix to shows that the proposed jollity chatbot system is more robust and can identify the user intents appropriately.

User Perception of Text-Based Chatbot Personality.

Abstract: This work explores the effect of chatbot personality on user experience and investigates how users perceive agent personality when conveyed through text. Building on previous work in the field of human-computer interaction on designing chatbot personality, we investigate whether users in a low-stakes conversation have a preference for a specific personality type when the agent does not use voice, is not visually represented, and does not provide identity cues such as gender. We developed two chatbots that interact with users in a multi-turn conversation and designed them to have distinct personalities along two axes of the Five Factor Model (extraversion and agreeableness). We conducted a user study to evaluate user engagement, user perception of the agents, and the effect of user personality on user experience.

A Cognitive Model for Emotion Awareness in Industrial Chatbots

Abstract: Industrial applications are increasingly adopting conversational agents (chatbots) for tasks ranging from primitive conversation interfaces to intelligent human assistants. In this emerging field of research, there has been a strong drive towards modelling human-like characteristics and behaviors in chatbots. However, only a limited number of research endeavors have focused on a chatbot for automatic characterization of end-user emotions. In order to address this limitation, we propose an artificial intelligence based cognitive model for emotion awareness in chatbots using Markov chains, word embedding, and Natural Language Processing. The proposed model is able to extract emotions from conversations, detect emotion transitions over time, predict real-time emotions and intelligently profile human participants based on their distinct emotional characteristics. We conducted experiments using a real-world end-user dataset to demonstrate the functionality of the proposed model. Results from experiments confirm the plausibility of this model for emotion awareness in industrial conversational agents.