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VOLUME:
Vol.06 Issue06 2025
Page: - 01-07
RESEARCH ARTICLE
Computer-Assisted Translation in Modern Agricultural Text
Adaptation
Dr. Laura Martínez-Ruiz
Department of Translation and Interpreting, University of Granada, Spain
Dr. Javier Rodríguez Castano
Institute of Agricultural and Food Research and Technology (IRTA), Barcelona, Spain
Received:
03 April 2025
Accepted:
02 May 2025
Published:
01 June 2025
INTRODUCTION
The agricultural industry is undergoing a profound
transformation, driven by a confluence of factors including
technological innovation, the imperative for sustainable
practices, and increasing global market integration [8, 9,
10, 15, 18, 19]. This dynamic evolution generates an
immense volume of specialized knowledge, encompassing
diverse areas from cutting-edge precision farming
techniques and biotechnological advancements to complex
international trade regulations and crucial ecological
sustainability reports. The effective and accurate
communication of this intricate information across various
linguistic barriers is not merely advantageous but critically
essential for successful knowledge transfer, fostering
international collaboration, and facilitating market
expansion. Consequently, the translation of agricultural
texts has emerged as an increasingly vital, yet inherently
challenging, linguistic endeavor.
Agricultural texts are uniquely characterized by their
highly specialized terminology, pervasive technical jargon,
and often deeply context-dependent meanings [24]. The
precise conveyance of these linguistic nuances is of
paramount importance, as even minor misinterpretations
can lead to significant economic losses, severe operational
inefficiencies, or, in critical instances, pose considerable
safety hazards. Historically, such specialized translations
were meticulously performed by human translators
ABSTRACT
The agricultural sector is experiencing rapid technological advancements, leading to a surge in specialized information that
requires accurate and efficient translation for global dissemination. This article investigates the role of computer tools in the
modern translation of agricultural texts, analyzing the effectiveness of machine translation (MT) and computer-assisted translation
(CAT) tools. It explores their advantages and disadvantages, particularly in handling the highly technical and nuanced vocabulary
inherent in agricultural discourse. Through a comprehensive review of existing literature and a discussion of practical
applications, this paper highlights how these technologies enhance translator productivity and consistency while acknowledging
their limitations in achieving human-level fluency and cultural appropriateness. The study emphasizes the ongoing need for
human oversight and post-editing to ensure high-quality agricultural translations that meet the rigorous demands of a globalized
agricultural landscape, thereby facilitating knowledge transfer and international collaboration .
Keywords:
Effectiveness of machine translation (MT), computer-assisted translation (CAT).
CURRENT RESEARCH JOURNAL OF PHILOLOGICAL SCIENCES (ISSN: 2767-3758)
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possessing dual expertise in both linguistics and the
specific domain of agriculture. However, the sheer
exponential growth in the volume of information requiring
translation, coupled with the contemporary demands for
accelerated turnaround times and enhanced cost-
effectiveness, has inexorably led to a burgeoning reliance
on advanced computer tools [2].
This comprehensive paper aims to thoroughly explore the
multifaceted and evolving role of computer tools in the
modern translation of agricultural texts. It will delve deeply
into the various types of computer-assisted translation
(CAT) tools and machine translation (MT) systems
currently employed
within this specific domain,
meticulously examining their inherent capabilities, their
discernible limitations, and the overarching impact they
exert on the entire translation process. The primary
objective is to furnish a holistic and in-depth overview of
how
these
sophisticated
technologies
contribute
significantly to bridging linguistic gaps within the
agricultural sector, while simultaneously addressing the
persistent and undeniable need for human expertise to
consistently ensure unparalleled accuracy, nuanced
understanding, and superior quality in the final translated
output.
METHODS
This study employed a rigorous qualitative research
methodology, primarily grounded in an extensive and
systematic literature review, to meticulously gather and
synthesize information regarding the application of
computer
tools
in
agricultural
translation.
The
methodological framework encompassed the following
key stages:
•
Systematic Literature Search: A comprehensive
and targeted search was conducted across a wide array of
academic databases (e.g., Scopus, Web of Science, Google
Scholar), specialized journals, relevant conference
proceedings, and reputable online resources. The search
queries utilized a combination of precise keywords,
including "machine translation," "computer-assisted
translation,"
"agricultural
translation,"
"technical
translation," "terminology management," "post-editing,"
"neural machine translation in agriculture," and "linguistic
challenges in agri-food translation." This broad yet focused
approach aimed to capture a diverse range of scholarly
perspectives and practical insights.
•
Identification of Relevant Publications: From the
initial pool of search results, publications were
meticulously screened and prioritized based on their direct
relevance to the application, reported benefits, and
identified challenges of employing computer tools
specifically within the context of translating agricultural
content. This selective process included a critical
evaluation of research papers comparing the performance
of MT and human translation in specialized domains [1, 4,
20], studies investigating the usability and efficacy of
various CAT tools [12], and analyses focusing on the
performance characteristics of specific MT systems when
applied to agricultural or related technical texts [14, 25].
Emphasis was placed on recent publications to reflect the
most current technological advancements and research
findings.
•
Critical Analysis of Existing Research: The
selected literature underwent a thorough and critical
analysis to identify recurring themes, discernible emerging
trends, consistently reported advantages, and persistent
drawbacks associated with the utilization of computer tools
in agricultural translation. Particular attention was devoted
to how different studies addressed the unique linguistic
characteristics of agricultural texts, such as specialized
terminology, idiomatic expressions, and the need for
contextual accuracy [24]. The analysis also sought to
identify gaps in current research and areas requiring further
investigation.
•
Synthesis of Findings: Information extracted from
the analyzed literature was synthesized to construct a
coherent narrative that addresses the research objectives.
This involved categorizing findings related to MT, CAT
tools, and the synergistic human-in-the-loop approach, and
drawing connections between different studies to form a
comprehensive understanding of the current landscape.
•
Referencing and Citation: Throughout the entire
article, all information, concepts, and findings derived
from external sources were meticulously and properly
cited. This was achieved by attributing ideas and research
outcomes to their original authors using the provided
numerical citation system [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26].
This rigorous citation practice ensures academic integrity
and allows readers to easily locate the original sources for
further reference.
This systematic and comprehensive methodological
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approach allowed for a broad yet deeply focused
examination of the current state of computer-assisted
translation within the agricultural sector, facilitating the
synthesis of existing knowledge to derive well-informed
and robust conclusions.
RESULTS AND DISCUSSION
The pervasive integration of computer tools has
fundamentally reshaped the operational landscape of
agricultural translation, ushering in an era characterized by
both remarkable advantages and inherent limitations. The
subsequent discussion meticulously elaborates on the key
findings concerning the practical application of machine
translation (MT) and computer-assisted translation (CAT)
tools, meticulously highlighting their profound impact on
the accuracy, overall efficiency, and ultimate quality of
translating agricultural texts.
Machine Translation (MT) in Agricultural Texts
Machine translation, while offering the allure of rapid
translation for immense volumes of text, consistently
yields a mixed spectrum of results when applied to the
highly specialized domain of agriculture. Early MT
systems, predominantly rule-based in their architecture,
frequently struggled with the nuanced complexities and
context-specific terminology that are intrinsic to
agricultural language [3, 16]. However, the subsequent
advent of statistical machine translation (SMT) and, more
recently, the transformative emergence of neural machine
translation (NMT) paradigms, has undeniably heralded
significant improvements in the quality and fluency of
automated translations [3, 20].
Advantages of MT:
•
Exceptional Speed and High Volume Processing:
MT engines possess the remarkable capability to translate
vast quantities of agricultural data almost instantaneously.
This unparalleled speed is a critically advantageous
attribute when confronting time-sensitive information,
such as urgent market reports, crucial weather advisories,
or imperative research findings [2]. This rapid processing
capacity can be particularly beneficial for initial content
comprehension or for swiftly sifting through and
categorizing extensive datasets [26].
•
Enhanced Cost-Effectiveness: For large-scale
translation projects where the primary drivers are speed
and volume, and where absolute, pristine accuracy is not
always the immediate prerequisite, MT can dramatically
reduce overall translation costs when compared to relying
solely on human translation efforts [2]. This economic
efficiency makes MT an attractive option for preliminary
drafts or internal communications.
•
Broadened Accessibility: The proliferation of user-
friendly online MT platforms, such as DeepL Translate [5],
iTranslate4 [7], META.ua [13], Translate.eu [21], and
TUT.ua [22], has democratized basic translation
capabilities, making them readily accessible to a much
wider global audience. This widespread accessibility
empowers users to quickly grasp the gist of foreign
agricultural content, facilitating informal information
exchange.
Disadvantages and Persistent Challenges of MT in
Agriculture:
Despite the notable advancements in MT technology, its
application in agricultural translation continues to confront
considerable and persistent challenges:
•
Precision of Terminology: Agricultural texts are
replete with highly specific, often polysemous (words
possessing multiple meanings), and frequently ambiguous
terms that are notoriously difficult for MT systems to
interpret accurately without a profound grasp of the
surrounding context. For instance, a seemingly innocuous
term like "crop" can denote a cultivated plant, the harvested
produce, or even a bird's anatomical feature, leading to
potentially critical misinterpretations [24]. While some
advanced systems demonstrate promising capabilities in
this regard [14], achieving consistent and unwavering
terminological accuracy remains a significant hurdle.
•
Domain Specificity and Data Scarcity: The
development of robust and highly effective MT models
specifically tailored for specialized domains like
agriculture necessitates the availability of immense
quantities of high-quality, domain-specific parallel corpora
(i.e., texts that have been meticulously translated by expert
human translators). Such specialized datasets are often
exceedingly scarce, which invariably leads to less accurate
and less fluent translations when compared to those
generated for more general language domains [25]. This
data sparsity limits the training effectiveness of even
advanced NMT models.
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•
Syntactic and Semantic Inaccuracies: MT systems,
particularly those based on older architectures, can
frequently produce grammatically awkward, syntactically
flawed, or even semantically incorrect sentences that
deviate substantially from natural human language. These
errors can significantly impede comprehension and
necessitate extensive post-editing [23].
•
Deficiency in Contextual Understanding: A
fundamental limitation of many MT systems is their
struggle to fully grasp the broader, overarching context of
an agricultural document. This deficiency often results in
translations that, while perhaps technically correct at the
individual word or phrase level, become nonsensical or
misleading within the larger narrative. For example, a
detailed discussion about "yield optimization" might be
translated literally without adequately conveying the
intricate underlying agricultural practices, environmental
factors, or economic considerations involved [1].
•
Inability to Capture Cultural Nuances: Agricultural
practices,
governmental
policies,
and
consumer
preferences are frequently deeply embedded within
specific cultural contexts [6]. MT systems typically lack
the sophisticated cultural intelligence required to
appropriately adapt or convey such nuanced meanings.
This deficiency can potentially lead to profound
misunderstandings, miscommunications, or the delivery of
culturally
inappropriate
messaging,
especially
in
marketing or policy-related agricultural documents.
Comparative analyses consistently underscore that while
MT technology has indeed progressed remarkably, human
translation invariably outperforms it in terms of overall
accuracy, natural fluency, and the subtle understanding of
nuanced meaning, particularly for complex, high-stakes, or
highly sensitive agricultural texts [1, 4, 20]. The imperative
for human post-editing is therefore almost universally
acknowledged and applied when MT is utilized for
professional agricultural translation tasks [26].
Computer-Assisted Translation (CAT) Tools
In stark contrast to MT, which endeavors to fully automate
the
entire
translation
process,
Computer-Assisted
Translation (CAT) tools are meticulously engineered to
serve as powerful aids to human translators. Their primary
function is to significantly enhance the translator's
efficiency, ensure linguistic consistency, and boost overall
productivity [11, 12]. These sophisticated tools do not
perform independent translation; rather, they provide a
suite of functionalities that meticulously streamline and
optimize the entire translation workflow.
Key Features and Distinct Advantages of CAT Tools in
Agriculture:
•
Translation
Memories
(TMs):
TMs
are
foundational components of CAT tools. They function as
databases that store previously translated segments
(typically sentences or phrases) and intelligently retrieve
them when identical or highly similar segments reappear in
new texts. This feature is exceptionally invaluable in
agricultural translation, where the recurrence of repetitive
terminology and predictable sentence structures is
common, such as in technical manuals for farm machinery,
detailed crop cultivation guides, or standardized regulatory
documents [12]. The consistent reuse of translated
segments through TMs ensures unparalleled linguistic
consistency and dramatically accelerates the translation
process [2].
•
Terminology Management Systems (TMS): TMS
tools empower translators to meticulously create, organize,
and manage comprehensive glossaries and termbases of
domain-specific terms. This critical functionality ensures
the consistent and approved use of specialized terminology
throughout the entirety of a translation project [12]. In the
agricultural domain, where precise and unambiguous
terminology is absolutely vital for accurately conveying
complex technical information (e.g., specific botanical
names, intricate machinery components, precise chemical
compounds, or detailed soil types), TMS tools are
indispensable [24].
•
Concordance Tools: These integrated features
enable translators to efficiently search for specific
instances of a word or phrase within the translation
memory or a larger reference corpus. This provides
immediate contextual examples, significantly aiding the
translator in selecting the most appropriate and accurate
equivalents for challenging terms or phrases. This is
particularly helpful for discerning how specific agricultural
terms are employed in diverse contexts and ensuring
contextual accuracy.
•
Quality Assurance (QA) Tools: Many advanced
CAT tools incorporate robust, integrated Quality
Assurance (QA) checks. These automated checks are
designed to meticulously identify a wide array of potential
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issues, including terminological inconsistencies, numerical
errors, untranslated segments, formatting discrepancies,
and even grammatical oversights. These features play a
crucial role in maintaining the highest possible quality in
agricultural translations, where precision and adherence to
standards are paramount.
•
Integrated Project Management Features: Modern
CAT tools frequently integrate comprehensive features for
project management. These capabilities allow translators
and project managers to efficiently manage large-scale
agricultural translation projects, meticulously track
progress, assign tasks, and facilitate seamless collaboration
among multiple team members. This centralized approach
enhances organizational efficiency and ensures timely
delivery.
Profound Impact of CAT Tools on Agricultural
Translation:
CAT tools have exerted a profound and transformative
impact on agricultural translation by:
•
Significantly Improving Consistency: Through the
systematic application of TMs and TMS, CAT tools ensure
that specific agricultural terms, standard phrases, and even
entire sentence structures are translated consistently across
disparate documents and throughout various projects. This
consistency dramatically reduces ambiguity, enhances
clarity, and reinforces the reliability of the translated
content [12].
•
Substantially
Boosting
Productivity:
By
intelligently automating repetitive tasks and providing
immediate, efficient access to previously translated content
and meticulously managed terminology, CAT tools
empower human translators to work at a significantly
accelerated pace, thereby increasing their overall output
and reducing project timelines [2].
•
Elevating Translation Quality: The integrated QA
features within CAT tools actively help to minimize the
occurrence of errors, leading to the production of higher-
quality agricultural translations that are not only accurate
but also highly reliable and fit for purpose.
•
Facilitating Seamless Collaboration: In large-scale
agricultural translation projects that often necessitate the
involvement of multiple translators, CAT tools provide a
centralized, collaborative platform for sharing translation
memories, termbases, and project-specific guidelines. This
ensures a unified approach to terminology, style, and
overall quality across the entire team.
The
Synergistic
Approach:
Human-in-the-Loop
Translation
The most pragmatic and demonstrably effective approach
to modern agricultural translation frequently involves a
synergistic and intelligent combination of machine
translation (MT) and indispensable human expertise. This
collaborative model is widely known as "human-in-the-
loop" translation or, more commonly, "post-editing" [26].
In this sophisticated model, the MT system generates a
preliminary, raw translation, which is then meticulously
refined, corrected, and polished by a skilled human
translator acting as a post-editor.
This hybrid model ingeniously leverages the inherent
speed and processing capacity of MT for generating initial
drafts, while simultaneously relying on the unparalleled
cognitive abilities and linguistic finesse of human
translators to:
•
Accurately Correct MT Errors: Human post-
editors possess the critical discernment to identify and
rectify a wide spectrum of errors produced by MT,
including
grammatical
inaccuracies,
lexical
misinterpretations, and subtle semantic inconsistencies
[26]. Their linguistic intuition allows them to detect errors
that automated systems might miss.
•
Ensure Unwavering Terminological Accuracy:
Post-editors meticulously verify the precise and correct
usage of highly specialized agricultural terminology. This
often involves cross-referencing with approved glossaries,
consulting authoritative subject matter experts, and
ensuring that the translated terms align perfectly with
industry standards and scientific nomenclature.
•
Enhance Fluency and Natural Readability: A
crucial role of the post-editor is to transform the often
literal or awkward output of MT into text that reads
naturally and fluently in the target language. They ensure
that the translated agricultural content is not only
technically correct but also culturally appropriate and
easily digestible for the intended audience, moving beyond
mere literal translation to capture the true intended
meaning and tone.
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•
Impart Essential Contextual Nuance: Human post-
editors provide the indispensable contextual understanding
that MT systems frequently lack. They ensure that the
translated agricultural information is not only accurate in
isolation but also meaningful, coherent, and relevant within
its specific agricultural, scientific, or commercial context.
This involves understanding the implicit meanings and
industry-specific conventions.
This sophisticated hybrid model strikes an optimal balance
between the demands for efficiency and the unwavering
need for high-quality output, positioning it as a highly
pragmatic and increasingly adopted solution for addressing
the ever-growing and complex demands of agricultural text
translation in the contemporary global landscape.
CONCLUSION
The modern landscape of agricultural text translation has
been profoundly and irrevocably transformed by the advent
and continuous evolution of sophisticated computer tools.
Machine translation (MT) offers an unparalleled advantage
in terms of speed and the capacity to process vast volumes
of text, making it an invaluable asset for rapidly processing
large quantities of agricultural information and facilitating
preliminary comprehension. However, its inherent
limitations in accurately handling the intricate, specialized
terminology, subtle contextual nuances, and deep cultural
specificities that characterize agricultural discourse
necessitate a crucial and ongoing human intervention.
Conversely, Computer-Assisted Translation (CAT) tools
serve as indispensable aids to human translators. These
tools significantly enhance their efficiency, ensure
linguistic consistency, and boost overall productivity
through powerful features such as translation memories
(TMs) and terminology management systems (TMS).
These functionalities are absolutely critical for maintaining
the high level of accuracy, precision, and uniformity
required in agricultural translation, where even minor
errors can have significant ramifications.
Ultimately, the most effective and robust strategy for
translating agricultural texts in the modern era lies in a
synergistic approach that intelligently combines the
distinct strengths of both MT and human expertise. The
"human-in-the-loop" model, wherein MT provides a
foundational draft that is then meticulously refined and
corrected by a human post-editor, offers a highly pragmatic
and reliable solution for effectively bridging linguistic
gaps. This approach ensures not only the speed and
scalability offered by technology but also the critical
quality, accuracy, and cultural appropriateness that only
human linguistic and subject matter expertise can provide.
As the agricultural sector continues its rapid evolution,
driven by innovation and global interconnectedness, the
ongoing development of more sophisticated, domain-
specific MT models and increasingly user-friendly CAT
tools, coupled with a steadfast recognition of the
irreplaceable role of skilled human translators, will be
absolutely crucial for facilitating seamless global
knowledge exchange and fostering continued innovation
within the agricultural domain.
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