A Literature Survey of Recent Advances in Chatbots
- URL: http://arxiv.org/abs/2201.06657v1
- Date: Mon, 17 Jan 2022 23:08:58 GMT
- Title: A Literature Survey of Recent Advances in Chatbots
- Authors: Guendalina Caldarini and Sardar Jaf and Kenneth McGarry
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Chatbots are intelligent conversational computer systems designed to mimic
human conversation to enable automated online guidance and support. The
increased benefits of chatbots led to their wide adoption by many industries in
order to provide virtual assistance to customers. Chatbots utilise methods and
algorithms from two Artificial Intelligence domains: Natural Language
Processing and Machine Learning. However, there are many challenges and
limitations in their application. In this survey 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.
Related papers
- A Complete Survey on LLM-based AI Chatbots [46.18523139094807]
The past few decades have witnessed an upsurge in data, forming the foundation for data-hungry, learning-based AI technology.
Conversational agents, often referred to as AI chatbots, rely heavily on such data to train large language models (LLMs) and generate new content (knowledge) in response to user prompts.
This paper presents a complete survey of the evolution and deployment of LLM-based chatbots in various sectors.
arXiv Detail & Related papers (2024-06-17T09:39:34Z) - Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community [57.56212633174706]
The ability to interact with machines using natural human language is becoming commonplace, but expected.
In this paper, we chronicle the recent history of this growing field of spoken dialogue with robots.
We offer the community three proposals, the first focused on education, the second on benchmarks, and the third on the modeling of language when it comes to spoken interaction with robots.
arXiv Detail & Related papers (2024-04-01T15:03:27Z) - History of generative Artificial Intelligence (AI) chatbots: past,
present, and future development [1.6019538204169677]
The study traces key innovations leading to today's advanced conversational agents, such as ChatGPT and Google Bard.
The paper highlights how natural language processing and machine learning have been integrated into modern chatbots for more sophisticated capabilities.
arXiv Detail & Related papers (2024-02-04T05:01:38Z) - Creation Of A ChatBot Based On Natural Language Proccesing For Whatsapp [0.0]
The objective of this study is to develop a chatbots based on natural language processing to improve customer satisfaction and improve the quality of service provided by the company through WhatsApp.
The results of this study will provide a solid foundation for the design and development of effective chatbots for customer service.
arXiv Detail & Related papers (2023-10-10T18:54:15Z) - Evaluating Chatbots to Promote Users' Trust -- Practices and Open
Problems [11.427175278545517]
This paper reviews current practices for testing chatbots.
It identifies gaps as open problems in pursuit of user trust.
It outlines a path forward to mitigate issues of trust related to service or product performance, user satisfaction and long-term unintended consequences for society.
arXiv Detail & Related papers (2023-09-09T22:40:30Z) - 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) - Conversational agents for learning foreign languages -- a survey [0.0]
Conversational practice, while crucial for all language learners, can be challenging to get enough of and very expensive.
This paper provides an overview of the chatbots for learning languages, critically analyze existing approaches, and discuss the major challenges for future work.
arXiv Detail & Related papers (2020-11-16T12:27:02Z) - An ontology-based chatbot for crises management: use case coronavirus [0.0]
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.
arXiv Detail & Related papers (2020-11-02T09:30:51Z)
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.