Rocío Ávila Ramírez

doi:10.19080/RAEJ.2025.06.555692

Mini Review

AI Challenges and Tools in Translation and Interpreting: a Brief Overview

Rocío Ávila Ramírez*

University of Cordoba, Spain

Submission:June 02, 2025;Published:June 16, 2025

*Corresponding author:Rocío Ávila Ramírez, University of Cordoba, Spain

How to cite this article: Rocío Ávila R. AI Challenges and Tools in Translation and Interpreting: a Brief Overview. Robot Autom Eng J. 2025; 6(4):555692.DOI: 10.19080/RAEJ.2025.06.555692

Introduction

Artificial Intelligence is everywhere these days – from your phone to your car. Learning AI isn’t just for tech experts anymore. It’s becoming a must-have skill for anyone who wants to stay ahead [1]. In today’s ever-evolving technological landscape, harnessing the power of AI models has become imperative. A Deloitte survey shows 94% of companies believe AI is critical for their business [2]. Besides, since 2018, European Union requires that algorithms used in decision support systems should provide explanations, which is known as “right to explanation” [3]. However, when AI is applied to translation and interpretation studies, it does not appear that the system provides an explanation for its decision-making process, such as why a particular translation is chosen or the rationale behind its selection. Understanding the intricacies of interpretative decisions made by AI would assist translators and interpreters in improving text correction and gaining a better understanding of how the system functions itself. This knowledge would contribute to greater rigor in their work and in the final product.

How Do AI Models Work

Artificial intelligence systems work by using algorithms and data. First, a massive amount of data is collected and applied to mathematical models, or algorithms, which use the information to recognize patterns and make predictions in a process known as training. Once algorithms have been trained, they are deployed within various applications, where they continuously learn from and adapt to new data. This allows AI systems to perform complex tasks like image recognition, language processing and data analysis with greater accuracy and efficiency over time [4].

Challenges in AI in translation

Artificial intelligence is very important for translators and interpreters because it helps them become more efficient and accurate in their work. AI can provide quick translations, allowing professionals to save time and focus on more complex or specialized tasks. Additionally, AI tools can improve the quality of translations by suggesting options and correcting errors, resulting in more precise and reliable work. For this purpose, the training of AI is essential to know precisely what to request from the system and what to expect from it and to be used by translators and interpreters in the most efficient way possible for their professional or academic work.

Following Shahmerdanova [5], these challenges, rooted in linguistic complexity, cultural nuances, and ethical considerations, underscore the limitations of AI-powered tools in certain contexts. Addressing these issues is essential to ensure that AI translation systems support accurate and culturally sensitive communication.

AI translation tools often struggle to interpret idiomatic expressions, metaphors, and context-dependent phrases. These linguistic features, deeply embedded in cultural and social contexts, frequently extend beyond their literal meanings, posing difficulties for AI systems reliant on statistical patterns and training data [3].

AI translation tools often lack the ability to recognize and preserve cultural nuances, leading to a homogenization of linguistic diversity. Human translators bring a deep understanding of cultural context, historical subtleties, and emotional tone, which machines currently cannot replicate [6]. This limitation is particularly problematic when translating culturally specific idioms, greetings, or formalities, where misinterpretation can result in unintended offense or loss of meaning [5].

Besides, AI translation systems face significant ethical challenges, particularly in sensitive fields like medical, legal, and diplomatic translation. These areas require precision, accountability, and a deep understanding of specialized terminology, which AI tools often fail to provide [7].

Methods of AI Translation

In order to offer an overview of the different methods in AI translation we show the next table where can be seen the description of each method and its pros and cons (Table 1).

The first method is Machine Translation (MT) which uses software tools and algorithms to convert the source text into a target language automatically. Is efficient but needs the human cooperation to accurate the final text.

The second is Machine Learning based translation and is a method of Artificial Intelligence Translation that utilizes algorithms and models to enhance the translation process. This method is versatile and powerful but requires more information and data to improve algorithms.

Thirdly we find Neural Machine Translation (NMT) which is a method of AI translation that uses deep learning techniques and neural networks to translate the meaning of text and speech. Is now in its early stages but could be revolutionary in translation by using AI.

Finally, we find Natural language processing (NLP). NPL is a branch of artificial intelligence focused on understanding, analysing and generating human language. It has a variety of applications, including search engine optimization, automated customer service, and text translation.

AI Translation Tools

After a brief reviewing the existing methods, we will present the tools mostly used and available for translation through AI. The choice between one or another will depend on the desired level of accuracy, speed, or optimization. We will present DeePL, Reverso and ChatGPT.

DeePL Translator is a machine translation service known for its high-quality, natural-sounding translations, particularly between European languages. It uses a Neural Machine Translation (NMT) engine that considers entire sentences and context for more accurate results than traditional word-by-word translation.

Reverso is specialized in language tools that offers a variety of services aimed at enhancing language learning and translation. Their offerings include online translation, contextual dictionaries, grammar and spell checkers, and conjugation aids, all designed to facilitate language learning and communication. Additionally, Reverso provides a mobile application for translating and learning languages, making their services accessible on the go.

ChatGPT is an advanced AI chatbot developed by OpenAI that excels in generating human-like dialogue. Utilizing sophisticated machine learning algorithms, it processes and analyses extensive datasets to craft responses to user queries. This language model is adept at comprehending both written and spoken human language, enabling it to interpret input accurately and produce coherent outputs. For instance, when a student submits a question, ChatGPT delivers a clear and understandable answer, adaptable to various formats and specific requirements.

Conclusion

The incorporation of machine intelligence into translation practices has marked the beginning of a new period characterized by greater accessibility, improved efficiency, and broader scalability. Tools driven by AI have transformed how translations are performed by facilitating instant communication, lowering expenses, and extending language services to people and organizations across the globe. Nonetheless, despite these significant innovations, there are important obstacles to consider. AI often finds it difficult to grasp idiomatic phrases, cultural subtleties, and vague situations, which can result in errors, but it can help save time and improve the efficiency of the translator.