1. CONVERSATIONAL AI: AN
OVERVIEW OF TECHNIQUES,
APPLICATIONS & FUTURE SCOPE
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com

2. Today's Outline
Introduction
Natural language understanding
Dialogue management
Natural language generation
Applications
Conclusion
Future work

3. 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...

5. 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.

6. Introduction
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...

7. 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...

8. 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...

10. Each of the aforementioned components is a difficult research
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.

11. 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...

12. 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...

13. Named Entity Recognition (NER) and Intent Classification are the two fundamental
tasks in NLU (IC).
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14. Dialogue
Management Dialogue Management (DM) is an
important module in the Conversational AI
framework that is responsible for
regulating the behaviors of the
Conversational Agent and translating
inputs to appropriate 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...

15. 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...

16. 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:

18. Natural Language
Generation
Natural Language 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...

19. 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.

20. With an expansion in research and development
in this domain over the last couple decades,
conversational AI applications 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
Applications

21. Conclusion
In order to obtain an understanding of this
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.

22. The work 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
Future Work
Contd...

23. 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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