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Modern Development Trends of Chatbots Using Artificial Intelligence (AI)
Conference Paper · October 2021
DOI: 10.1109/ITMS52826.2021.9615258
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2021 62nd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS) | 978-1-6654-0615-4/21/$31.00 ©2021 IEEE | DOI: 10.1109/ITMS52826.2021.9615258
Modern Development Trends of Chatbots Using
Artificial Intelligence (AI)
Julija Skrebeca
Institute of Information Technology
Riga Technical University
Riga, Latvia
julija.skrebeca@gmail.com
Paula Kalniete
Institute of Information Technology
Riga Technical University
Riga, Latvia
paula.kalniete@inbox.lv
Janis Goldbergs
Institute of Information Technology
Riga Technical University
Riga, Latvia
j.goldbergam@gmail.com
Liene Pitkevica
Institute of Information Technology
Riga Technical University
Riga, Latvia
liene.pitkevica@edu.rtu.lv
Darja Tihomirova
Institute of Information Technology
Riga Technical University
Riga, Latvia
darjatihomirova3@gmail.com
Andrejs Romanovs
Dept. of Modelling and Simulation
Riga Technical University
Riga, Latvia
andrejs.romanovs@rtu.lv
Abstract—It is said 80% of business owners will use chatbots
in the future. Chatbots become more and more popular in terms
of business, that is why it is necessary for businesses implement
innovative approaches to provide customer service twenty-four
hours. Such business especially is needed in terms of the
challenging Covid-19 times. Artificial Intelligence-powered
chatbots can work as intelligent teaching systems, for providing
a personalized way of learning for students. Chatbot reviews
student's responses and his learning progress. One of the most
convenient features of chatbots is the opportunity to send lecture
materials in the form of messages to students as if it is just a chat
with a friend. Apart from personalized chatbot usage in the
studying process, it can be used to streamline business processes,
e.g., such as sales. Nowadays chatbots are able to help customers
to search products they need, place orders to the cart and pay
for it, and track delivery processes of orders. Chatbot
intelligence is being developed every day and every year now, so
very soon chatbots will be able to perform even more difficult
tasks to make the life of the user easier. In this paper, authors
will deliver theoretical materials and historical background of
chatbots, describe classification and techniques of chatbots, then
describe the modern development trends of chatbots using
artificial intelligence (AI), and finally discuss the role of chatbots
in education and e-commerce.
Keywords— artificial intelligence (AI), chatbots, covid-19, ecommerce, education, trends
I. INTRODUCTION
A chatbot can be represented as a computer, program,
algorithm, or artificial intelligence which the main goal is a
communication with a person, or another interlocutor.
Chatbots might be developed to communicate by replying to
simple keywords or to interact with a user by supporting
specific topics. They can be used for a customer service,
marketing, advertising, entertainment industry, data collection
etc. For example, a chatbot can communicate with customers
in online shops and help them to find the product they need.
In this paper, authors have done research in chatbot history
to find out how the first chatbots were made, which methods
were used, and how much chatbots have been enhanced in the
last few decades. Also, the authors have referred to the most
common methods and techniques that are used to create a
chatbot. In this paper, there is mentioned classification of
chatbots and offered both classification and methods to actual
chatbot trends in the Covid-19 era.
978-1-6654-0615-4/21/$31.00 ©2021 IEEE
The term chatbot – chat(-ter) bot was invented by Michael
Loren Mauldin. According to Shawar and Atwell’s simplified
definition, chatbots are artificial intelligence-assisted chat
applications whose functions range is from answering simple
questions to participating in complex conversations [10].
A chatbot is a software application that helps to carry on
a conversation using text-based or auditory methods [5].
Programs in Chatbots are developed to mimic human
conversations. Chatbots are used for variety of purposes,
including customer service, routing request, and information
retrieval. By using Natural language processing, some
chatbots can be used for word classification.
The simplest botts processed basic user messages and
requests. In a posted conversation with the user, with an
algorithm, it responds with pre-programmed answers.
Chatbots are most frequently used by dialog systems, such as
customer support.
Artificial Intelligence (AI) simulates human intelligence
in the form of various devices. Such AI devices are developed
to behave as humans do and imitate their actions. Artificial
Intelligence stands for machines that exhibit natural
characteristics similar to the human mind, such as problem
solving and understanding.
Natural Language Processing (NLP) is the program
interface
that
allows
computers
and
humans
intercommunicate with each other. NLP makes it possible for
computer to analyze vast amounts of language data from
different sources. NLP technique developers can improve the
chatbot knowledge to process various tasks such as text
analysis, stemming, text summarization, automatic
summarization, topic extraction, text mining, speech
recognition, translation, segmentation, and automatic question
answering.
NLP combines two techniques – natural language
generation (NLG) and natural language understanding (NLU).
NLP is one of the forms of the Artificial Intelligence that
enables chatbots to process users’ text or audio messages and
give a proper answer. NLP has the following layers:
• Application.
• Data Storage.
• NLP Engine.
• Platform for Data Lake.
NLP contains two important techniques to work properly:
• NLU – Natural Language Understanding maps user
given input to its useful representations. It analyzes
unusual phenomena of language.
• NLG – Natural Language Generation is used for text
and sentence planning, mining, and text realization.
NLU is the process of generating meaningful responses in
the form of natural language, and includes:
• Text planning retrieves important data from the
database.
• Sentence planning selects the required words, produces
relevant phrases using these words and creates
meaningful sentences.
• Text implementation is the flow mapping of the
sentence according to the sentence structure.
It is much harder to realize Natural Language
Understanding process than execute the Natural Language
Generation process.
Natural Language Toolkit (NLTK) was programmed for
the creation and application of symbolic and statistical NLP in
Python. It is a pack of libraries that include parsing and
classification of text, text processing for tokenization,
stemming of words, and reasoning about semantics.
Keras is an API library for neural networks in Python. It
combines well with R, PlaidML, TensorFlow, and the
Microsoft Cognitive Toolkit.
Tkinter is an interface for developing graphical user
interface (GUI) applications.
Tensorflow is a software library framework that focuses
mainly on machine learning. It uses data flow graphs and
differentiates programming across different numbers of tasks
to build models. It is used to create large-scale development
applications including neural networks. It is mainly used for
classification, understanding, prediction, and creation.
Nowadays there are many chatbots, but how did they start?
What was the first chatbot?
Eliza is one of the most famous and at the same time oldest
chatbots invented by the Artificial Intelligence Laboratory in
MIT, dating back to 1964. Eliza inspired many developers in
the field to develop their own chatbots. In the early scenario
called DOCTOR, the chatbot Eliza has the role of a Rogerian
psychotherapist, asking open-ended questions, which she also
answers, drawing attention from herself to the user.
PARRY is another well-known chatbot developed in
1972. This program, which diverts attention from itself, uses
an opposite strategy than Eliza. It does not behave like a doctor
but like a paranoid schizophrenic patient. It tries to provoke
controversy and makes the interlocutor give more detailed
answers [1].
Racter is another knowable chatbot (short for raconteur –
a storyteller). This Chatbot generates prose in the English
language. Prose can be a short text, not written in the verse, in
which the authors present fragments of their existential
experience. This chatbot uses logical sentences based on its
knowledge and makes conclusions one by one. A good
example was written by Racter: “A crow is a bird, an eagle is
a bird, a dove is a bird. They all fly in the night and in the day.
They fly when the sky is red and when the heaven is blue.
They fly through the atmosphere. We cannot fly. We are not
like a crow or an eagle or a dove. We are not birds. But we can
dream about them. You can.” [1].
The same method was used with a chatbot called
Jabberwacky designed to mimic a natural human conversation
using a voice-activated system.
Another milestone in the development of chatbots was
called Dr. Sbaitso chatbot. It was able to synthesize speech –
in some ways it became even more human-like than its
predecessors, even if it could not converse in a more
complicated and complex way. Such problem of chatbot
communication insufficient complexity still exists today.
Kuki - one more chatbot that needs to be represented
(formerly known as Mitsuku) that claims to be a young girl of
18 years from England [9]. Kuki is developed using AI MarkUp Language and both - Retrieval and Generative-Based
approaches which allows her to imitate a human conversation,
avoiding answering the same question over and over again.
The most popular chatbots at the time are chatbots that
are used for e-commerce and e-education solutions. There
have always been difficulties in communication between
customer and seller or student and teacher as sometimes there
might be situations where employees' workload is huge and
answering all these questions can cause a big delay although
most probably the question can be answered quickly, and the
information might be found easily. This is where chatbots can
be helpful to reduce communication problems in terms of
response time. An e-commerce chatbot can assist to find the
product that a customer is searching for or help to fill a parcel
returning template. For students, chatbots can answer
questions that are related to due dates or requirements in a
specific subject. Chatbots are very common nowadays and
they are going to improve and become more helpful and
necessary in the future.
Currently, there are plenty of well-known chatbots that are
used in daily life. For example, Siri – a virtual assistant of iOS
interface that uses natural language to answer user inquiries
and perform web service requests. Google Now is another
mobile application for Android and iOS devices that provides
Google-developed predictive maps with information and daily
updates to automatically answer users’ questions. Cortana the Windows voice platform with information that helps
software and hardware developers. Chatbots for Messenger a program that uses Artificial Intelligence (AI) to realize an
interaction with customers. Facebook launched the platform
to allow developers to produce chatbots that are able
communicate with Facebook users through the Messenger
chat interface. Messenger chatbots understand users’
questions and formulate responses in a very human way [3].
Nowadays, functional chatbots are no longer a rarity. They
are being developed more and more advanced. One more
interesting project is Sophia, the chatbot that hides behind the
appearance of a humanoid female robot with highly advanced
facial expressions [1].
Interesting research was done by a group of students from
the Philippines - chatbots also can help to deal with emotional
traumas during rehabilitation or as a prevention of an
emotional breakdown by simulating empathy [6]. Such
chatbots have two types of perceived input data - typed text
and user’s facial expression transmitted via video. For now,
such chatbots powered by Deep Learning can recognize
neutral, happy, and sad users’ facial expressions with 16.7%,
66.7%, and 56.7% accuracy respectively. The combination of
textual and video data input provides a chatbot with sufficient
information to create a proper answer in a certain
conversation. So, it is even possible that in the future chatbots
will be able to assist psychologists or even psychiatrists during
patient treatment.
II. CHATBOT CLASSIFICATION AND TECHNIQUES
Chatbots are classified (Fig. 1) by their interaction,
information gathering, and usage goal types, e.g., Text-Based
and Voice-Based for interaction mode, Open Domain and
Closed Domain for knowledge domain, Task-Oriented and
Non-Task-Oriented for goals, and Rule-Based, RetrievalBased, Generative-Based for design approach [7], [9]. Some
of them are shortly described further.
Task-Oriented chatbots (e.g., Alexa, Cortana, Siri) goal
is to help a user to fulfill his task. Task-Oriented chatbots work
in restricted domains and are made to help with a hotel or
flight booking, create a schedule, or find specific information,
etc.
Non-Task-Oriented chatbots can handle an extended
conversation with the user, which helps to create a feeling of
interaction with a human. These responses of chatbots can be
divided into two approaches – Generative-Based (generates
more proper responses during the conversation) and RetrievalBased (learns to select more informative responses from a
repository of a current conversation). None of them can
answer if there is no pattern for that question, because they do
not create responses, but only take predefined ones.
Domain-Specific chatbots, which include Open-Domain
(used for a non-specific conversation) and Closed-Domain
(conversation has a specific goal) Approaches, are used in
specific areas (e.g., education and health care) which helps to
raise their efficiency and quality of given answers.
Text-Based and Voice-Based chatbots differ by their
interaction type with users. Speech-to-text is one of the most
crucial functions in any competitive chatbot and its
improvement. The basic idea of speech-to-text consists of
criteria like vocabulary size – the number of words in
vocabulary which are millions from different languages.
Another criterion is speaker independence or in other words
chatbot’s ability to recognize speakers. Co-articulation is an
important part of speech-to-text because a chatbot must have
the ability to process a continuous stream of words that
requires segmentation and tokenization of the speaker’s input.
The chatbot must handle noise to filter out background noises
and must be able to understand input when a person is talking
at different distances from the microphone [4].
Fig. 1. Classification of chatbot development approaches
There are Rule-Based and Self-Learning (includes
Generative-Based, Retrieval-Based Approaches) chatbots [7],
[9]. Any of the two Self-Learning chatbot approaches can
have different techniques such as:
• Parsing method chooses exact words from the input
text, makes them less sophisticated, and uses them for
output.
• Pattern Matching method takes as a pattern user’s
input and gives him the most suitable response stored
in the template.
• Artificial Intelligence Mark-up Language is made of
data objects (AIML elements) that consist of topics and
categories. Categories have patterns (matches the user
input) and templates (creates an output) which provide
a flexible conversation.
• Markov Chain Model uses probabilities to know
what kind of input data will be given to a chatbot next.
Mostly used for imitating simple human conversation.
Cannot be used for an extended conversation in a
specific area.
• Artificial Neural Networks Models were made to
develop smarter chatbots and can use both Generative-Based and Retrieval-Based approaches.
Such chatbots learn how to interact from conversations
with a human. Deep Learning allows imitating a
human brain's work to process given data and produce
new patterns.
Even though none of all chatbots passed the Turing Test
since the first one (ELIZA) had been invented and chatbots
based on Deep Learning are not an exception, nowadays the
most promising technique for chatbot development is Deep
Learning. It is believed that Deep Learning combined with a
Retrieval-Based Approach can improve the chatbot
intelligence and give results that will show a significant
difference compared to previous results.
III. CONTINUOUS IMPROVEMENT STUDY OF CHATBOT
TECHNOLOGIES USING A HUMAN FACTORS METHODOLOGY
A usability test was conducted to compare the usability of
three chatbot platforms – Watson by IBM, Pandorabot by
Pandora, and Verbot [2].
The research study was done by using these methods:
• Gathering demographic information of participants.
• Pre-test questionnaire.
• Video and/or audio record chatbot sessions.
• Post-test questionnaire.
For the purpose of the research study, the feedback was
obtained from ten participants and then assessed using a
System Usability Scale (SUS). Results indicated that Watson
by IBM was perceived as the most user-friendly platform
overall. Watson received an average SUS score of 81.875 out
of 100, Pandorabot scored 88.75 out of 100. Verbot did not
receive a SUS score as far as none of the ten participants chose
this platform. While Pandorabot scored higher at SUS, 80%
of the participants preferred Watson platform. The statistical
discrepancy between these participants’ responses was
attributed to the fact that only a small group of participants
chose Pandorabot. Research results showed that Watson by
IBM is the chatbot that best matches with human factors
analysis. Watson chatbot had perceived intelligence, a
simplified atmosphere, and was chosen by 80% of
participants.
IV. CHATBOT STATS AND TRENDS SHAPING BUSINESSES IN
2021
In many service industries chatbots are used to answer
customers’ questions and help them navigate a company’s
website. Well-developed and sophisticated chatbots rely on
machine learning, which is a computer program that
continually improves through its usage, and Natural Language
Processing (NLP), which helps solve the problems of
mimicking human-generated text and speech.
Chatbots that feel increasingly humanlike are already
being widely deployed. It all happens thanks to Natural
Language Processing (NLP). NLP allows chatbots to interact
with users using in complete sentences that have a natural
conversational flow. Dialects, small sounds, deliberate pauses,
or even misspelling can help customers feel more
comfortable. Like that conversation seems to be more realistic
and lifelike. This, in turn, makes customer service easier for
everyone involved and helps companies further improve
customer experience and loyalty [14].
It is important to avoid any misunderstanding when a
chatbot must give correct and important information. The
main chatbot challenges are:
• Misunderstanding
requests.
Chatbots
often
misunderstand users’ requests because they are unable
to understand the will of the consumer.
• Inaccurate execution of commands. Chatbots cannot
respond to any technical commands issued by
customers.
• Difficulty understanding accents. Chatbots are not yet
able to understand accents or dialects to identify the
correct intent of the user [13].
AI technology is evolving every day, which suggests that
chatbots are also subjects that are in a constant state of change.
Nowadays, there are some chatbot trends in business and other
areas. The statistics show that the healthcare, finance, travel,
education, and real estate industries are benefiting the most
from chatbots:
• By 2021 about 80% of businesses are expected to
integrate some form of chatbot system.
• Chatbots are able to help businesses save up to 30% on
customer support costs.
• The market value of chatbots was $703 million in
2016.
• More than 50% of customers expect businesses to be
open and answer their questions 24/7.
• Chatbots are popular among both – Millennials and
Baby Boomers. There are more than 300,000 active
chatbots on Facebook.
Exist four main issues that contribute to skepticism about
the chatbot usefulness. Quite simply, chatbots cannot execute
technical commands that the user types. Unfortunately, for
now chatbots are unable to properly process a customer’s
intent, resulting in misinterpreted queries and responses.
Chatbots do not have enough of conversational intelligence,
meaning they often do not process the implied details of a
dialog, resulting in an inadequate conversation. As well,
chatbots are unable to understand different accents and
cultural meanings to provide a correct response. AI chatbot
trends are poised for profound change that will impact several
key business processes. These processes include:
• Automation of business processes.
• Prediction of costumer behavior.
• Recommendation of products and services.
• Streamlining customer support experiences [16].
The increasing expectations of twenty-four hours chatbot
availability means that chatbots must be able to analyze user’s
speech, text, and even facial expressions. Eventually, chatbots
will be used in a variety of consumer applications and internal
business functions [8], [15].
V. CHATBOT ROLE IN EDUCATION
The use of chatbots in education would make life easier
during Covid-19. When all training in the educational
establishment takes place in remote mode. The use of
chatbots could send statements about exams and systems
could help with the understanding of the teaching substance.
The Institute of Technology in Georgia, USA received
around 10,000 questions about the conduct of training. The
number of questions was difficult to answer in time. So,
Ashok Goel invented the chatbot Jill. Chatbot answered many
different questions: about the format of the exam, possible
topics, and deadlines. Teachers answered difficult questions,
but an algorithm was invented for simple questions. The
chatbot was trained on 40,000 specific issues raised over the
past few years. When the answers generated 97% accuracy,
Chatbot went online [10].
VI. CHATBOTS ROLE IN E-COMMERCE AND EVALUATION
METHODS
E-Commerce has grown in Latvia during Covid-19. This
has significant implications for society and business
worldwide. Consumers are attracted to the fact that there is a
wider choice of goods at much lower prices than customers
would pay in a local shop, as well they are attracted by the
idea that they can shop anywhere all around the world, taking
advantage of exchange rates and economic differences. But
there is also a significant disadvantage, e.g., many customers,
mostly people from older generations, do not trust online
shopping on the Internet, because they cannot check the
quality of the product and ask about the warranty [11]. The
chatbot would help preserve the cash of various large e-shops,
by starting to use advisers to answer client questions. And
therefore, it is crucial to be able to determine how good your
chatbot is. There is a wide variety of chatbot evaluation
methods that determine the performance of commercially
available chatbots. This paper describes two - PARADISE
and Kuligowska. PARADISE estimates subjective factors
such as ease of usage, clarity, naturalness, friendliness,
robustness reading misunderstanding, and willingness to use
the system again. This framework maximizes task
effectiveness and minimizes dialogue cost by determining
overall performance:
Chatbots for educational processes (Fig. 2) should have
knowledge in a specific area be it math, physics, and
chemistry, or literature, music, and geography.
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = ∝ ∗ 𝑁𝑁(𝑘𝑘) − ∑𝑛𝑛𝑖𝑖=1 𝑊𝑊𝑖𝑖 ∗ 𝑁𝑁(𝐶𝐶𝑖𝑖 )
where α is the weight on k, each cost function is weighted by
Wi and N is a Z score normalization function. Kuligowska
method measures the chatbot in 8 categories – visual look,
implementation way of chatbots, either it is a built-in
window, pull out tab or both, giving the highest score for both
GUI. This framework quantifies the bot’s ability to produce
speech, either it has a unique custom voice, has text-to-speech
modules without custom voice, or can communicate only
with text, giving the highest score for bots with custom voice
modules. The fourth category is both abilities to answer
domain-based questions like “What products are you
selling?”. Are chatbots able to lead a coherent dialogue and
understand the dialogue’s context [4]?
It is important to not just build and launch a chatbot but
to be sure that the chatbot is engaging and helpful, otherwise,
buyers would not interact with the chatbot. Statistics say that
about 40% of users will stop interacting with a bot after the
first message, and 25% of users after the second. To avoid
these poor statistics, it is necessary to measure chatbot
activity to make sure that the bot is benefiting buyers and
driving sales. This analytic data includes basic metrics that
indicate the bot’s helpfulness, like retention rates, advanced
metrics, and engagement rates to measure sales indication. To
be able to collect and analyze sales data, there are several
options on how to achieve it, like using analytics tools in
chatbot building platforms or integrating chatbot to an
external analytics platform [12].
VII. CHATBOT DEVELOPMENT TECHNIQUE
Since 1964, when the first chatbot Eliza was invented,
chatbots, their classifications, and architecture have been
developing dramatically. During Covid-19 pandemic times
chatbots became even more popular than before due to the
massive digitalization of many industries such as Ecommerce, finance, health care, education, traveling, etc.
The research showed the importance of chatbots in
everyday life, so it is very necessary to use their capabilities
to make our lives better and easier during this tough pandemic
time. Chatbots for E-commerce and education will be very
useful for businesses and students all around the world, but
such chatbots must have special approaches as far as they
have different goals of usage.
Fig. 2. Educational Chatbot structure
Closed-Domain class chatbots, which are used for
conversations in specific areas, fit such descriptions
perfectly. The Retrieval-Based approach of Self-Learning
chatbots will help an educational chatbot to specialize better
and generate much more proper responses than GenerativeBased chatbots that tend to create wrong or illogical
responses due to its complexity. Educational chatbots must
give adequate and accurate information in the context of the
educational process, even if communication will be based on
predefined and repeated phrases. As a technique, the AI
Mark-Up Language - has its own data architecture that
consists of objects, topics, categories, response patterns, and
templates which is very similar to the educational process,
where students have different subjects and topics. Each
category has a special rule that matches the user’s input and
allows a proper answer.
Even though E-commerce chatbots have a little bit
different goals and direction of usage, they can have almost
the same structure as education chatbots have (Fig. 3). It
should be a Closed-Domain class chatbot that will be able to
consult clients in any E-commerce area starting with clothes
and electronic equipment and ending with trading and
insurance. Closed-Domain class allows to have knowledge in
a specific area which will enlarge chatbot usage benefits and
increase clients’ satisfaction. E-commerce chatbots need to
be Self-Learning with a Generative-Based approach to adapt
to purchase tendencies and know the user's preferences to
offer better products for a certain client. Even though
Generative-Based
chatbots
sometimes
tend
to
“misunderstand” their interlocutor and can make a mistake
while responding to the client, it is very necessary for Ecommerce chatbot to give different answers as far as they are
not predefined to give client a feeling of communication with
a real consultant. Due to a wide selection of goods, the
chatbot must “see” differences between two similar products
to offer a better one that matches the client's preferences the
most. Deep Learning can help to provide such a personalized
attitude towards a customer due to its ability to create its own
“opinion”, which can help to provide a client with a product
or service that client needs.
research on chatbot trends connected to the health care and
chatbot users’ emotional state.
As a result of the survey were defined work principles for
educational and E-commerce chatbots. Both are SelfLearning chatbots, but use different approaches, and
developed using AILM (for educational chatbot) and Deep
Learning (for E-commerce chatbot) techniques which fit the
goal of each chatbot.
REFERENCES
[1]
[2]
[3]
[4]
[5]
Fig. 3. E-commerce Chatbot structure
As an example of a well-developed chatbot was used
Kuki (former Mitsuki) which combines Retrieval and
Generative-Based approaches and uses AI Mark-Up
Language. But the usage of Generative-Based approach in
Educational chatbot will not be that suitable due to this
approach unstable work. E-commerce chatbot will be less
interesting and useful for clients as well, if it will be
developed using the AI Mark-Up Language method due to its
inflexibility.
To sum up, chatbot classes can differ due to goals which
chatbots must reach, but nowadays the most popular chatbots
are the Self-Learning ones, which can offer dynamic and
human-like communication. Among techniques the most
progressive and promising are connected to AI, e.g., Deep
Learning, which is used in many different areas, has all
chances to develop an improved chatbot performance so they
can seem to be humans during the conversation.
VIII. CONCLUSION
E-commerce and education chatbots have different usage
goals. In the educational process chatbots help with
organizational issues, e.g., they provide students with
information about the format and date of exams, possible
milestones and their deadlines. In E-commerce chatbots help
customers by answering their questions, e.g., inquire about
fabrics, ask about delivery duration etc. Even though chatbots
can have different goals of usage they can be similar in
structure. Chatbots can belong to a closed-domain class
which can provide a person with information in any area. For
better performance chatbots are better to be self-learning with
a generative approach to adapt to human needs. So, the thing
that differs the most often are approaches of chatbots. Due to
the big number of existing approaches, chatbot developers
can choose any that will fit the goal of communication with a
chatbot. It is worth saying that the most promising approach
for chatbot improvement is Deep Learning that can make the
far future closer. In the future it is planned to continue the
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