Conversational AI:An Overview of Techniques, Applications & Future Scope - Phdassistance
                     CONVERSATIONAL AI: AN  
OVERVIEW OF TECHNIQUES,  
APPLICATIONS & FUTURE 
SAnC AcaOdemPic pEresentation by
Dr. Nancy Agnes, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
Today's 
Outline Introduction
Natural language 
understanding  Dialogue 
management
Natural language 
generation  Applications
Conclusion  
Future 
work
Conversational AI is a sub-domain of AI that  
deals with speech-based or text-based AI  
agents that can imitate and automate  
conversations and verbal interactions.
Due to two major advancements,  
conversational AI  agents such as chatbots  
and voice assistants have multiplied.
Contd...

On the one hand, the methods required to develop highly accurate AI models,  
such as Machine Learning and Deep Learning, have advanced significantly as a  
result of increased research interest in these fields, as well as progress in  
achieving higher computing  power through the use of complex hardware  
architectures such as GPUs and TPUs.
Second, conversational agents have been considered as a natural fit in a wide  
range of applications such as healthcare, customer service, ecommerce, and  
education due to their Natural Language interface and the nature of their 
design.
Introductio
n
Conversational AI Agents have become mainstream today due to significant  
advancements in the methods required to build accurate models, such as  
machine learning and deep learning, and, secondly, because they are seen as a 
 natural fit in a wide range of domains, such as healthcare, ecommerce,  
customer service, tourism, and education, that rely heavily on natural language 
 conversations in day-to-day operations.
This rapid increase in demand has been matched by an equally rapid rate of  
research and development, with new products being introduced on a daily  
basis.
Contd...
The exponential growth in study interest in this topic, on  
the other hand, has brought to light several interesting,  
but ephemeral, research prospects.
As a result, a systematic record of the key principles of  
Conversational AI, traditional methodologies and current  
implementations in these domains, as well as continuing  
research, is critical.
This will serve as a platform for future research and  
advancements. Conversational AI is made up of three  
primary components, each of which is subdivided into  
basic pieces that conduct more preliminary tasks.
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Contd...
Conveying the current state and results to the other engaging entity is the  
final step in a Conversational AI engagement.
The user should receive the response in an easily understood format. This is 
 accomplished through the usage of Natural Language Generation (NLG).
It is the process of transforming structured data into natural language that  
can be understood by humans. It works in direct opposition to normal  
language compehension.
Content determination, document structuring, aggregation, lexical choice,  
referring expression development, and realization are all parts of the 
process.
Contd...

Each of the aforementioned componentsis a difficultresearch  
challenge in and of itself.
To improve the accuracy of each component, various machine  
learning and deep learning models are applied.
This paper examines current research on natural language  
interpretation, dialogue management, and natural language  
generation in conversational AI bots, as well as some of the 
potential  future avenues for Conversational AI.
Natural 
Language  
Understanding
Natural language understanding (NLU) is a field of artificial 
intelligence  (AI) that uses computers to interpret unstructured text 
or speech as  input.
Natural language understanding (NLU) is an essential and difficult 
subset  of natural language processing (NLP).
NLU is entrusted with conversing with untrained people and 
deciphering  their intentions, which means it interprets meaning rather 
than just  interpreting words.
Contd...
Even common human errors like as mispronunciations or transposed letters or  
words are not enough for NLU to discern meaning.
The NLU allows for direct human-computer communication.
The NLU enables computers to understand human languages without the usage 
of  if/else statements.
Natural Language Understanding (NLU) addresses one of AI's most
difficult  problems.
Contd...
Named Entity Recognition (NER) and Intent Classification are the two fundamental 
 tasks in NLU (IC).
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Dialogue  
Managemen Dialogue Management (DM) is
t an  important module in the Cfraomnvewrsoartionatlh aAtI is responsible for  
k  the behaviorsof the
rCeognuvlaetrisnagtional Agent and translatin
 inputs to appropriate g
outputs.
The DM system is in charge of creating an 
 interaction strategy that will lead the 
agent  in determining its own actions 
based on  the inputs received from the 
user.
Contd...
Goal/Task Oriented Systems and Non-Task Oriented Systems are the two 
sorts of  DM systems.
Object-oriented DM Systems are in charge of moving users from one state of  
discussion to the next in order to complete a specified or dynamically 
understood  task.
When the Conversational Agent is in control of the conversation, the DM system  
also acts as a state tracker, continuously maintaining the conversation's state 
and  initiating a transfer from one state to another.
Contd...
Table 1 shows the various situations in which a discussion can be in  
during a conversation between a human and a Conversational Agent.
Some of the classic, current state-of-the-art and promising
Dialogue  Management System implementation approaches are as 
follows:

Natural 
Language  
GNeatunrael rLangtuiaogen Generation (NLG) is a subdomain of Natural 
Language  Processing that focuses on natural language answer generation 
methods.
NLG is crucial in Conversational AI because it makes the dialogue feel more  
natural for the human participant, which is a critical component in  
determining the effectiveness of Conversational Agents.
The Dialogue Management system sends structured data to the NLG 
module,  which is based on the dialogue history and present context .
Contd...
As a result, the natural language sentence or text 
produced  by the NLG component in a Conversational 
Agent is also  the final output of the Conversational AI 
framework for  each dialogue occurrence.
The NLG component's output is based on the Natural  
Language Understanding and Dialogue Management  
Systems' processing and outcomes.
With an expansion in research and 
Application development  in this domain over the last 
couple decades,  conversational AI applications 
s have proliferated.Conversational Agents are being used in a wide  
range of applications to execute a variety of  
activities. Ashay Argal et al developed a chatbot  
in the tourist industry using DNN (Deep Neural  
Network) and Restricted Boltzmann Machine  
(RBM).
Kyungyong Chun et al. created an AI-powered  
conversational agent that used a cloud-based  
knowledge base to provide an online healthcare 
 diagnosis service
Conclusio
In order to obtain an understanding of this  
n domain's evolution,  the study offered classic  
methodologies for Conversational AI  
implementation.
The article on each of the three essential  
components of Conversational AI Agents, 
namely  Natural Language Understanding, 
Dialogue  Management, and Natural Language 
Generation,  was also reviewed in this article.
Future 
WTheo wrorkk given in this paper serves as a springboard for 
future  study in Conversational AI, which can go in a variety of 
ways.
This article has analyzed some of the flaws in current  
Conversational AI implementations while also presenting 
some  of the current research being complete to address 
these flaws.
This ongoing  study can be combined with simultaneous  
implementations that aid in the general acceptance of these  
research works while also allowing them to be tested in real-  
world circumstances
Contd...
The state-of-the-art works discussed in this paper are the product of a variety 
of  research projects.
Future work can be done to combine all of these state-of-the-art component-  
level works into single hybrid architecture capable of performing 
extraordinarily  well on all Conversational AI tasks, as well as determining the 
compatibility  between these different research works.
Finally, as discussed in this article, Conversational AI applications in fields such  
as healthcare, education, and tourism can be further developed by combining  
Conversational AI with other AI subdomains such as Computer Vision to  
investigate tasks such as visual question answering and language-controlled  
image segmentation.
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