Title | Chatbot: A Deep Neural Network Based Human to Machine Conversation Model |
Publication Type | Conference Paper |
Year of Publication | 2020 |
Authors | Vamsi, G Krishna, Rasool, Akhtar, Hajela, Gaurav |
Conference Name | 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) |
Date Published | jul |
Keywords | chatbot, conversational agent, conversational agents, Deep Learning, feature extraction, Human Behavior, machine learning, machine learning classification technique, Medical services, Metrics, natural language processing, Neural networks, pubcrawl, Scalability, Software, Task Analysis, Training |
Abstract | A conversational agent (chatbot) is computer software capable of communicating with humans using natural language processing. The crucial part of building any chatbot is the development of conversation. Despite many developments in Natural Language Processing (NLP) and Artificial Intelligence (AI), creating a good chatbot model remains a significant challenge in this field even today. A conversational bot can be used for countless errands. In general, they need to understand the user's intent and deliver appropriate replies. This is a software program of a conversational interface that allows a user to converse in the same manner one would address a human. Hence, these are used in almost every customer communication platform, like social networks. At present, there are two basic models used in developing a chatbot. Generative based models and Retrieval based models. The recent advancements in deep learning and artificial intelligence, such as the end-to-end trainable neural networks have rapidly replaced earlier methods based on hand-written instructions and patterns or statistical methods. This paper proposes a new method of creating a chatbot using a deep neural learning method. In this method, a neural network with multiple layers is built to learn and process the data. |
DOI | 10.1109/ICCCNT49239.2020.9225395 |
Citation Key | vamsi_chatbot_2020 |