=Paper=
{{Paper
|id=Vol-3295/paper2
|storemode=property
|title=Researching Etiquette Figures of Hercule Poirot's Character Using Javascript Libraries React and Node.js
|pdfUrl=https://ceur-ws.org/Vol-3295/paper2.pdf
|volume=Vol-3295
|authors=Marta Karp,Nataliia Kunanets,Vasylyna Skorokhoda
|dblpUrl=https://dblp.org/rec/conf/itpm/KarpKS22
}}
==Researching Etiquette Figures of Hercule Poirot's Character Using Javascript Libraries React and Node.js==
https://ceur-ws.org/Vol-3295/paper2.pdf
Researching Etiquette Figures of Hercule Poirot's Character
Using Javascript Libraries React and Node.Js
Marta Karpa, Nataliia Kunanets a,b , Vasylyna Skorokhodaa
a
Lviv Polytechnic National University 12 Bandera street, Lviv, 79013, Ukraine
b
Ivan Franko National University of Lviv, Universutetska Street 1, Lviv, 79000, Ukraine
Abstract
The article examines the main linguistic features of Hercule Poirot’s speech etiquette figures,
particularly the French phrases, as well as the speciality of translation in the detective genre
of literature using computer linguistics, as computer linguistics and machine translation
issues and problems become more relevant as scientific and technological progress
accelerates in the modern world. Users’ needs for speedy, accurate translation of varied
information delivered electronically drive the development of translation programs. The
article discusses the aspects of combining Java-Script-Libraries React and Node.js to
construct a project for software products, as well as the process of creating a translation
program, and highlights the key responsibilities of machine translation, as well as its benefits
and drawbacks.
IT project management is the process of planning, organizing, and delineating responsibility
for the completion of a specific information technology goals. We have learned about
computational linguistics, machine translation, and artificial intelligence systems before
starting this project. Computational linguistics is thought to be a fringe area of linguistics
concerned with developing automated systems for storing, processing, and using linguistic
knowledge and information represented by natural language signs. Computational linguistics
aims to recreate information about and in language, allowing for the automation of human
intellectual functions and cognitive activity, as well as automated voice generation and
computer processing and recognition. Computational linguistics is a broad field that includes
the use of computer tools, such as programs, computer technologies for organization and data
processing, to model the functioning of language in specific situations and problem areas, as
well as the use of computer models of language in linguistics and related disciplines. Because
computer modeling of language is regarded a field of programming theory in the discipline of
linguistics, it only concerns applied linguistics in the latter sense. As a result, computational
linguistics, like applied linguistics, is a linguistic science that combines several scientific
domains, and its applied direction dictates how its work is applied to solve real-world
problems.
Keywords 1
IT project management, Javascript libraries, React, Node.js, computational linguistics,
machine translation, artificial intelligence systems
1. Introduction
The structure of the issue. Our research is both scientific and practical, because we researched
etiquette speech formulas in French and developed an automated online translator to make the
translation process easier. Machine translation is a complicated issue that has yet to be fully grasped.
Its findings are important, first and foremost, in terms of improving the translation process. The study
Proceedings of the 3rd International Workshop IT Project Management (ITPM 2022), August 26, 2022, Kyiv, Ukraine
EMAIL: martakarp26@gmail.com (Marta Karp); nek.lviv@gmail.com (Nataliia Kunanets); skorokhoda.v@gmail.com (Vasylyna
Skorokhoda)
ORCID: 0000-0002-7332-7739 (Marta Karp); 0000-0003-3007-2462 (Nataliia Kunanets); 0000-0002-9092-9571 (Vasylyna Skorokhoda)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
also underlines the unique characteristics of French word interpretation. It is important to remember
that detective fiction has its own distinct style, thus the translator should take care to preserve the
genre’s quirks when using computational linguistics to translate.
Analysis of recent studies and publications. O. Baranov, A. Biletsky, M. Karp, E. Karpilovska,
N. Kunanets, O. Selivanova and others have worked on computational linguistics research [1, 2, 3, 5,
10, 11, 12].
Computational linguistics, according to O. Selivanova, is “a fringe area of linguistics devoted to
developing automated systems for storing, analyzing, adapting, and using linguistic knowledge and
information represented by natural language signs.” Computational linguistics aims to recreate
information about and in language in order to automate intellectual operations and human cognitive
activity, as well as automated voice synthesis and computer processing and recognition [5].
Parts of the overall problem that have yet to be solved. The method of developing software for
the examination of Hercule Poirot’s speech etiquette figures is described in this article for the first
time.
The goal of the paper is to demonstrate how computational linguistics can be used to investigate
the speech etiquette figures of Hercule Poirot, the protagonist in the works of English author Agatha
Christie. The study’s major goal was to create software that could translate phrases from French to
Ukrainian.
2. Presentation of the key material
Computational linguistics is a scientific and engineering study concerned with the computational
interpretation of written and spoken language, as well as the construction of artifacts that may be used
to process and create language in large groups and in discourse. Computational comprehension of
language also provides an understanding of thinking and intelligence because language is a mirror of
the mind. Because language is our most natural and versatile mode of communication, linguistically
competent computers make it considerably easier for us to engage with machines and software of all
types, as well as helping us meet our requirements by processing massive volumes of textual data on
the Internet. The research was based on the work of renowned linguists A.O. Biletsky, E.A.
Karpilovskaya, and V.I. Perebyinis [2, 3, 4]. Computational linguistics aims to create software that
can understand natural language, or the language we use on a daily basis.
2.1. Using computational linguistics to prove and describe the problem's
solution
According to the analysis of the dialogues with Hercule Poirot, we have compiled a glossary of his
most frequent phrases and translated them from French into Ukrainian (see Table 1). So, we can
observe that the protagonist, Hercule Poirot, uses in his speech a range of different French phrases. In
the dialogues, there are comparisons, epithets, metaphors, etc. Analyzing the main character’s speech
etiquette, we are able to deduce that etiquette is represented by three types: intracultural, subcultural,
and intercultural. The whole set of national traditions and norms of behavior form the etiquette of
national speech. The following criteria are always needed to evaluate the rules of etiquette: degree of
formality / informality of communication; language passport of the partner (i.e., typical personality
parameters determined by speech – gender, age, social origin, occupation, profession, level of culture,
etc.); degree of relationship / acquaintance; communication; situation; genre and the whole genre of
speech; and the level of culture of the partner. The key category of etiquette seems to us to be the
category of politeness, i.e., the focus of communication on maintaining the dignity of the partner and
respecting the recipient. The personal factor has a significant impact on the level of ethical
communication. As a result, inner self-control and self-discipline, especially for people who are self-
centered, brutal, and inflammatory, minimize errors in etiquette and cultural traditions.
Table 1
List of speech etiquette figures used by Hercule Poirot’s character in French and their translation
into Ukrainian
Numeration Phrases in French Translation of phrases into
Ukrainian
A Mon cher / Ah! Mon cher! Мій дорогий
B Mais oui Саме так/ Так. Звичайно
C comme ça От-от
D La Sante Sophie Собор Святої Софії
E Eh bien По вагонах, мсьє
F Envoiture, Monsieur Ну
G Enfin! Нарешті
H Voila, Monsieur От, мсьє
I Merci, Monsieur Дякую, мсьє
J Jolie femme Красива жінка
K Voilà ce qui est embêtant А це вже дратує
L Très bien, Monsieur Чудово
M Eh bien/ehbien/Bien Добре; звичайно
N Mon vieux! Старий
O Les affaires – les affaires! Справи…справи!
P Précisément! Точно!
Q Comment? Як? Чому?
R Là- là Ну й ну
S Tout à fait au bout, Аж у кінець, мсьє
Monsieur
T Je crois qua vous avez un Гадаю, ви помилилися
erreur
U En voiture! Автомобілем!
V Elle est jolie—et chic Вона красива й елегантна
W Clientèle Клієнтура
X Ce n’est rien. Je me suis Нічого. Я помилився
trompé
Y De l’eau minérale, s’il vous Мінеральної води, будь
Plaît ласка
Z Bonne nuit, Madame На добраніч, мадам
A La dame américaine Та американська леді
B Bon soir, Monsieur Доброго вечора, мсьє
C Vous êtes un directeur de la Оскільки ви – директор
ligne, je crois, Monsieur. Vous цієї компанії, гадаю, ви
pouvez nous dire зможете нам сказати
D Pardon, Monsieur Вибачте, мсьє
E Chef de train Кондуктор
F Déjeuner Обід
G Ah! c’était terrible! О! То було жахливо!
H C’est une femme Це жінка
I Oui, Monsieur Так, мсьє
J C’est entendu Домовлено
K Le docteur Лікар
L “Qu’est-ce qu’il y a? У чому річ? Чому?
Pourquoi?
M Précisément Точно
N Que pensez-vous de ça? Що ви думаєте про це?
O Ah! c’est rigolo, tout ça! Ах! Це просто смішно!
P Mon Dieu Боже мій
Q Ah! quel animal! Ну й тварина!
R Tout de même Тим не менш
S Après vous, Monsieur Після вас, мсьє
T Mais non, après vous Ні, після вас
U Ce n’est rien. Je me suis Не хвилюйтеся. Я
trompé. помилився
V Cauchemar Страшний сон
W Voilà une grande dame Оце справжня леді
X Elle est jolie femme Вона красива жінка
Y En permission У відпустку
Z Ça se voit Це й видно
A Vous n’éprouvez pas Ви не відчуваєте емоції
d’émotion
B Encore un peu, Madame? Ще трохи, мадам?
C Mais il n’y a rien à voir Там нічого такого немає
D Ma foi О Господи!
E Tout de même, Все-таки
F Dans son caractère, Не в його характері
G Entrez Увійдіть
H C’est impayable Безцінна
I Chic Ефектний
J Canaille Негідник
K Vous êtes bien aimable, Ви дуже люб’язні, мадам
Madame
L Diable! Хай йому грець!
M Grande dame Великосвітська дама
N Qui s’excuse s’accuse Хто вибачається,
звинувачує
O Mademoiselle Мадмуазель
P Pardon, Monsieur le Comte Вибачте, пане графе
Q Précis Перелік
R Métier Професія
S Objet de luxe Предмет розкоші
T Premier service. Le dîner est Обслуговування першого
servi. Premier dîner класу. Вечерю подано.
Вечеря для першого класу
U Hors de combat Поза грою
V Mon cher, vous êtes Мій дорогий, ви – ви
épatant! C’est formidable дивовіжні! Це чудово
W Per Dio На Бога
X Roman policier Детективний роман
Y C’est rigolo Це дивно
Z Messieurs et mesdames Мадам та мсьє
A C’est possible Це можливо
B Protégée улюбленець; ставленик
C Fête вечірка; свято
D Flair стиль; відчуття; інтуїція
E Revivit відродитись
F mon ami Мій друже
G Quel horreur! Який жах!
H N’est ce pas? Це не воно?
I C’est vrai Це правда
J Pas mal Непогано
K Recherche Дослідження
L Pas encore Ще ні
M Comment? Як?
N Merci Дякую
O Pardon? Перепрошую
P Précisément точно
Q hors d’oeuvres закуски
R Mais oui… Ну так
S A tout à l’heure Побачимось (пізніше)
T Pauvre femme Бідна жінка
U Oui, c’est peut-être là Так, можливо
V Parbleu Чорт забирай
W Quelconque Будь-який
X Le type Хлопець, вид, чоловік
Y Faites attention Будьте обережні
Z Pas ça Нічого такого
A pour une femme Для жінки
B Du tout Зовсім,цілком
C A la bonne heure В потрібний час
D un peu трішки
E Ça, oui Так, так
F Bon Dieu Боже мій, заради Бога
G Mon Dieu! Боже мій! Господи!
H C’est trop tard Уже занадто пізно
I Mais qu’est ce que vous Що ти тут робиш?
faites là?
J Vous éprouvez trop У вас занадто багато
d’émotion емоцій
K Ah, c’est ingénieux, ça! Ах, ось це геніально!
L Eh bien І так, отже, ну
M crime intime Особистий злочин
N au courant В курсі
O Vous croyez Ви думаєте
P ces gens là! Такі люди, як він
Q Encore! Все ще! Досі!
R la chance Шанс, успіх,удача
S Inconnu Невідомий, незнайомець
T Et alors, je vais à la pêche А потім я йду рибалити
U Inutile Немає потреби
V Une bonne idée. Гарна ідея
W Enfin Нарешті
X au fond На дні, на глибині
Y mise en scène інсценування
Z Tout de même Все одно
A tout à fait à part Абсолютно окремо
B C’est tout naturel Це цілком природньо
C dernier cri Останній крик моди,
сучасний
D Du tout Зовсім, взагалі
E Bêtises Дурниці, нісенітниці
F bien entendu Само собою, зрозуміло,
звичайно
G C’est ingénieux. Tout de Це геніально. Але це
même c’est bien imaginé, ça. добре продумано.
H Mes enfants Мої діти
I Motif Мотив, причина
J A vous la parole! Цей поверх – твій!
K Mieux que ça, Навіть краще, ніж це, міс
mademoiselle.
L Petite Маленький, невеличкий
M Du tout! Зовсім! Взагалі! В
порядку!
N Rouge Червоний
O modus operandi Спосіб дій, методи
P bona fides добросовісно
Q Mon cher Мій дорогий
R Voilà Ось
S Bon Правильний, хороший,
відмінний,
добрий
T Mademoiselle Мадмуазель, міс
U mon enfant Моя дитина
V Alors c’est bien, mon enfant. Тоді це добре, дитя.
W Tout de même Все ж, тим не менш
X Quelle idée! Що за ідея! Чим я тільки
думав!
Y Mais si! Але! Та ну!
Z Rouge, impair, manque! Червоний, непарний,
відсутній!
A le sport спорт
B un coeur magnifique! Прекрасне серце!
C Vive le sport! Нехай живе спорт!
The study of ten Agatha Christie books served as the foundation for the practical portion of our
work. We produced phrases in French and translated them into Ukrainian for a full investigation of
the roles of the speech etiquette elements (hereinafter SE) of the Hercule Poirot character. Following
that, a tool for automating the translation of phrases from French to Ukrainian was created. We
learned about computer linguistics, machine translation, and artificial intelligence systems before
finishing this research.
Computational linguistics, according to A. Baranov, is a broad field that includes the use of
computer tools such as programs, computer technologies for organization and data processing, and the
use of computer models of language to model the functioning of language in specific situations and
problem areas, not only in the field of linguistics, but also in allied fields. It is only about applied
linguistics in the latter sense, because computer modeling of language can be considered a branch of
programming theory in the discipline of linguistics. As a result, computational linguistics, like applied
linguistics, is a linguistic science that incorporates other scientific domains, and its applied direction
dictates how its work is applied to tackle real-world problems [1].
The most important tasks of machine translation. It has long been a goal of computer science to
utilize computers to translate text from one language to another. Machine translation, on the other
hand, has only recently become a viable tool for a larger range of applications. This valuable
technology is made possible by advances in natural language processing, artificial intelligence, and
computing power. The practice of employing artificial intelligence (AI) to automatically translate text
from one language (source) to another (target) without the need for human interaction is known as
machine translation.
Since the 1950s, one of the first applications of computing power has been translation.
Unfortunately, the task’s complexity exceeded early programmers’ estimations, necessitating a
massive capacity for data processing and storage that far outstripped the capabilities of the earliest
computers. Basic machine translation was not possible until the early 2000s, when software, data, and
the necessary hardware became available. To “train” computers to translate text, the initial developers
used statistical language databases [7].
In 2016, Google performed an experiment to see if neural learning and artificial intelligence
models might be used to teach translation procedures. In several languages, the small team
methodology proved to be faster and more efficient when compared to Google’s basic statistical
machine translation system.
Google altered direction and selected neural machine translation as its primary development
paradigm because it proved to be so effective. Other big manufacturers quickly followed suit,
including Microsoft and Amazon, and modern machine translation has emerged as a viable
supplement to translation technology. Machine translation is currently included in many translation
management systems’ workflow solutions for their users. Any automation integrated into a traditional
computer translation tool (CAT tool) or a modern translation management system to automatically
undertake repetitive translation chores is referred to as automated translation. Content contains
triggers that alert the system that it can be automated. Inserting commonly used language, such as
legal notifications, into database documents, such as content management systems [7], is one example
of this.
Rule-based machine translation is one of the three most popular methods of machine translation.
The original version of rule-based machine translation had a number of fundamental flaws, including
the necessity for extensive human editing, the need to manually add languages, and low overall
quality. It is utilized in fairly simple situations where the meaning must be grasped quickly.
A statistical model of the associations between words, phrases, and sentences in a text is created
via statistical machine translation. It applies the model to the second language to transform these
elements into a new language. As a result, it improves rule-based machine translation while still
having many of the same issues. Neural machine translation is another sort of machine translation. As
previously stated, the neural model of machine translation, like neural networks in the human brain,
uses artificial intelligence to learn languages and continuously enhance this information. Neural
machine translation is fast becoming the standard in the development of the machine translation
engine in general [10].
Machine translation programs provide the following advantages:
1. Quick access and fast speed. Working with a translation firm frequently involves extra time and
effort, and the translation program is always available. We get a comprehensive translation of the
entire text in only a few seconds. This allows a person to rapidly grasp the overall message, and if the
application is set up to translate texts on the required themes, only minor tweaking is required.
2. Efficiency in terms of costs. If we hire professional translators, we will have to pay them based
on the number of pages they translate. You do not need to pay to use the online machine translation
system; all you need is internet access.
3. Information security and protection. Any information, including personal information, can be
trusted to the machine translation system (ie business correspondence, financial reports).
Confidentiality is guaranteed by the translation program.
4. Versatility and flexibility. The capacity to modify a certain topic area (specialist dictionaries) or
a single book or text is referred to as flexibility (user-created dictionaries). When a customer changes
the subject from fiction to scientific and technical materials, the translator usually specializes in that
field, thus mistakes are unavoidable. As a result, the machine translation system is widely used. The
user merely needs to connect the vocabulary that is focused on the appropriate themes correctly [10].
Machine translation programs, on the other hand, have a number of drawbacks:
1. Electronic translation algorithms can successfully translate simple portions of speech, but they
can’t always handle terminology, sentences, or colloquial speech.
2. Some electronic translation tools do not actually translate words; instead, they transliterate
them. In such circumstances, synonyms must be chosen and the sentence’s structure must be
rearranged. As a result, the translation is frequently revised or edited. And it’s a lot of labor that takes
a lot of time and effort [10].
In terms of machine translation system development in Ukraine, two systems have been well-
known since 2014: Ruta plaj (ProLing Office) and Pragma by Trident Software.
The creation of a project for a software product using the JavaScript libraries React and
Node.js. In the practical portion of our research, we designed a tool that allows a translator to search
for words and phrases in French in PDF files and translate them into Ukrainian using JavaScript
programming abilities and knowledge of JavaScript libraries. PDF files (works by Agatha Christie)
are converted during processing so that the application may read them as a line of code. After then,
the text is broken down into individual words. The words are then “filtered”. If the words compared
by the computer match the array of terms in the library’s dictionary, they are classified as French.
After that, a single word is translated. It’s worth mentioning that the application doesn’t only translate
single words; it also translates entire statements. There are various limitations to machine translation,
like with any other technology. Because a perfect translation requires the computer to be improved to
the point where it can differentiate French prefixes, suffixes, and affixes. Because some terms in
French are of English origin, the program incorrectly labels them as French, resulting in an incorrect
search. However, we want to develop the software product in the future and focus on resolving issues
that surfaced throughout the research (see Figures 1-3).
Figure 1: The commands to execute the application from the command line
Figure 2: The built website features an online translator with a demonstration of the translation
process using a selected work as an example
Figure 3: A portion of the computer code in the VSCode environment
The French Phrases Detector algorithm was created using the JavaScript programming language.
JavaScript is a text-based computer language that is used to make web pages interactive on both the
client and server sides. If HTML and CSS are the languages that provide web sites structure and style,
JavaScript adds interactive components that keep the user engaged. An Amazon search box, a news
summary video embedded in The New York Times, or an update to your Twitter feed are all instances
of JavaScript that you can use every day. By converting a static page into an interactive one,
JavaScript enhances the performance of a web page. As a result, JavaScript provides functionality to
web pages.
JavaScript is mostly used in web browsers and web applications. However, this programming
language is also utilized in software, servers, and embedded hardware controllers outside of the
Internet. Here are some of the most important JavaScript tasks: Web page interactivity; web and
mobile application development; web server development and server application development; game
development [8].
JavaScript is a scripting language used by many browsers to execute dynamic tasks on the Internet.
The impacts of JavaScript can be seen in the “Show on Click” drop-down menu, additional content
provided to the page, and dynamically changing colors of components on the page, including some
features. There would only be HTML and CSS on the web if JavaScript didn’t exist. They confine
you to a few web page implementations. 90% (if not more) of online sites would be static, with only
dynamic updates such as CSS-based animations [9] being used.
We chose JavaScript as our programming language due to its numerous benefits. There is less
contact with the server when using JavaScript, because you may examine the user’s input before
sending the page to the server. This reduces server traffic, resulting in a lower stress on your server.
Visitors don’t have to wait for the page to reload to check whether they forgot to type something
because they get immediate feedback. This programming language’s improved interactivity allows for
the creation of interfaces that respond when the user hovers over them or activates them with the
keyboard. Advanced interfaces let you utilize JavaScript to add features like drag-and-drop sliders to
give your site visitors a more appealing experience.
However, several faults in the program’s development were caused by a number of flaws in the
JavaScript programming language. The following are some of JavaScript’s limitations: JavaScript
cannot be considered a full-fledged programming language because it lacks key JavaScript features.
Because such functionality is not available, JavaScript cannot be utilized in network applications.
JavaScript has no support for multithreading or multiprocessing. JavaScript is a lightweight,
interpreted programming language for embedding interactivity into static HTML pages.
The developed program is built on top of popular JavaScript libraries like React and Node.js. The
first is used to create a back-end site, while the second is used to create a front-end site. React is a
user interface development library. React isn’t a framework, and it wasn’t even created with the web
in mind. It is used for visualization as well as collaborating with other libraries. React Native, for
example, can be used to make mobile apps, while React 360 can be used to make virtual reality
programs. There are many additional possibilities.
Developers use React in conjunction with ReactDOM to construct web applications. React and
ReactDOM are frequently discussed in the same context as other real-world web development
frameworks and are used to tackle the same problems.
The fundamental goal of React is to reduce the number of mistakes made when creating user
interfaces. This is accomplished by employing stand-alone logical code components that explain
various aspects of the user interface. These elements are then integrated to form a complete user
interface. The majority of the visualization effort is abstracted by React, allowing you to focus on the
design. React is a package that aids developers in the creation of user interfaces in the form of a tree
of individual components known as components. A component is a collection of HTML and
JavaScript that contains all of the code required to display a tiny portion of a bigger user interface.
Each of these elements can be mixed and matched to create increasingly sophisticated aspects of the
program. The rest is merely information.
Node.js (Node) is a server-side JavaScript execution platform that is open source. Node is ideal for
constructing real-time apps like chat, news feeds, and web messaging that require a continual browser
connection to the server.
Node.js is designed to run on a dedicated HTTP server with only one thread and one process active
at any given moment. Programs written with Node.js are event-driven and run asynchronously. The
Node platform generates code that does not follow the usual approach of receiving, processing,
sending, waiting, and receiving. Instead, Node sends small requests one after the other without
waiting for answers, processing incoming requests in a continuous stack of events. This is in contrast
to traditional models, which execute larger, more sophisticated processes and numerous threads at the
same time, with each thread waiting for a suitable response before proceeding.
According to Node.js author Ryan Dahl, one of the key advantages is that it does not impede
input/output. Some developers criticize Node.js, claiming that if a process demands a large number of
cycles, the application will be stalled, which can lead to program failure [6]. Because of the vast
number of tiny processes on which the Node code is based, proponents of the Node.js approach say
that CPU processing time is less of an issue.
The combination of these two frameworks served as the foundation for the creation of our program
project. The primary distinction is that Node.js is a server framework, whereas React.js is a user
interface framework. Both frameworks are widely used and have their own set of benefits and
drawbacks. When creating a server-side web application, such as an online streaming platform,
Node.js is the framework to use. When you need to construct a project with changing states, such as
dynamic inputs, buttons, and more, React.js is the way to go. Both frameworks can be used in the
same project. The Node.js framework may be used to create a backend, and React.js can be used to
create an interface. Netflix is the best example of framework integration. Both frameworks are backed
by a large and active community. It is up to you to decide which one is best for you based on your
needs and requirements.
3. Conclusions
To summarize, it is impossible to translate detective literature without taking into account the
genre peculiarities of the features in such works, especially when automated translation is used. The
accuracy and precision of the text content are the main challenges of machine translation. After all,
understanding the sequence of events in detective works necessitates a high-quality translation from
the original language. To understand the transfer concept processes, the translator must adhere to the
stylistic demands of this genre and remain detailed.
A solid understanding of JavaScript has been a prerequisite for developing our application. We
have also drawn on our understanding of Node.js, React, and machine learning principles. The
findings could aid in the advancement of knowledge in the field of natural language processing.
Reading through theoretical information in the topic frequently frustrates people, leading them to
abandon it before realizing its actual potential. However, we have attempted to describe the action
algorithms in a concise manner utilizing the frameworks and packages indicated. Learning about
React and Node.js provides a new set of abilities that makes working on web AI much easier.
A text can be translated into French using the automated translator we developed during our
research. The program project, on the other hand, still needs to be developed and refined. We want to
improve the program so that it can translate complete paragraphs of text rather than just individual
words and phrases.
4. References
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