An ontology-based chatbot for crises management: use case coronavirus
- URL: http://arxiv.org/abs/2011.02340v1
- Date: Mon, 2 Nov 2020 09:30:51 GMT
- Title: An ontology-based chatbot for crises management: use case coronavirus
- Authors: Khouloud Hwerbi
- Abstract summary: The project is to create a COVID Assistant to provide the need of up to date information to be available 24 hours.
This master thesis is dedicated to discuss COVID Assistant and explain each component in detail.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Today is the era of intelligence in machines. With the advances in Artificial
Intelligence, machines have started to impersonate different human traits, a
chatbot is the next big thing in the domain of conversational services. A
chatbot is a virtual person who is capable to carry out a natural conversation
with people. They can include skills that enable them to converse with the
humans in audio, visual, or textual formats. Artificial intelligence
conversational entities, also called chatbots, conversational agents, or
dialogue system, are an excellent example of such machines. Obtaining the right
information at the right time and place is the key to effective disaster
management. The term "disaster management" encompasses both natural and
human-caused disasters. To assist citizens, our project is to create a COVID
Assistant to provide the need of up to date information to be available 24
hours. With the growth in the World Wide Web, it is quite intelligible that
users are interested in the swift and relatedly correct information for their
hunt. A chatbot can be seen as a question-and-answer system in which experts
provide knowledge to solicit users. This master thesis is dedicated to discuss
COVID Assistant chatbot and explain each component in detail. The design of the
proposed chatbot is introduced by its seven components: Ontology, Web Scraping
module, DB, State Machine, keyword Extractor, Trained chatbot, and User
Interface.
Related papers
- LLM Roleplay: Simulating Human-Chatbot Interaction [52.03241266241294]
We propose a goal-oriented, persona-based method to automatically generate diverse multi-turn dialogues simulating human-chatbot interaction.
Our method can simulate human-chatbot dialogues with a high indistinguishability rate.
arXiv Detail & Related papers (2024-07-04T14:49:46Z) - Leveraging Large Language Models to Power Chatbots for Collecting User
Self-Reported Data [15.808841433843742]
Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts.
We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably.
arXiv Detail & Related papers (2023-01-14T07:29:36Z) - Neural Generation Meets Real People: Building a Social, Informative
Open-Domain Dialogue Agent [65.68144111226626]
Chirpy Cardinal aims to be both informative and conversational.
We let both the user and bot take turns driving the conversation.
Chirpy Cardinal placed second out of nine bots in the Alexa Prize Socialbot Grand Challenge.
arXiv Detail & Related papers (2022-07-25T09:57:23Z) - A Literature Survey of Recent Advances in Chatbots [0.0]
We review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used.
We highlight the main challenges and limitations of current work and make recommendations for future research investigation.
arXiv Detail & Related papers (2022-01-17T23:08:58Z) - A Deep Learning Approach to Integrate Human-Level Understanding in a
Chatbot [0.4632366780742501]
Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second.
We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence.
arXiv Detail & Related papers (2021-12-31T22:26:41Z) - Training Conversational Agents with Generative Conversational Networks [74.9941330874663]
We use Generative Conversational Networks to automatically generate data and train social conversational agents.
We evaluate our approach on TopicalChat with automatic metrics and human evaluators, showing that with 10% of seed data it performs close to the baseline that uses 100% of the data.
arXiv Detail & Related papers (2021-10-15T21:46:39Z) - Few-Shot Bot: Prompt-Based Learning for Dialogue Systems [58.27337673451943]
Learning to converse using only a few examples is a great challenge in conversational AI.
The current best conversational models are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL)
We propose prompt-based few-shot learning which does not require gradient-based fine-tuning but instead uses a few examples as the only source of learning.
arXiv Detail & Related papers (2021-10-15T14:36:45Z) - CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement
Learning [60.348822346249854]
This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns.
We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning.
To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy.
arXiv Detail & Related papers (2021-10-08T07:44:47Z) - Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn
Chatbot Responding with Intention [55.77218465471519]
This paper proposes an innovative framework to train chatbots to possess human-like intentions.
Our framework included a guiding robot and an interlocutor model that plays the role of humans.
We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
arXiv Detail & Related papers (2021-03-30T15:24:37Z) - "Love is as Complex as Math": Metaphor Generation System for Social
Chatbot [13.128146708018438]
We investigate the usage of a commonly used rhetorical device by human -- metaphor for social chatbots.
Our work first designs a metaphor generation framework, which generates topic-aware and novel figurative sentences.
Human annotators validate the novelty and properness of the generated metaphors.
arXiv Detail & Related papers (2020-01-03T05:56:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.