Use of NLP Techniques in Translation by C hat GPT : Case Study (2023)

Abstract: Use of NLP Techniques in Translation by ChatGPT: Case Study Natural Language Processing (NLP) refers to a field of study within the domain of artificial intelligence (AI) and computational linguistics that focuses on the interaction between computers and human language. NLP seeks to develop computational models and algorithms capable of understanding, analyzing, and generating natural language text and speech (Brown et al., 1990). At its core, NLP aims to bridge the gap between human language and machine understanding by employing various techniques from linguistics, computer science, and statistics. It involves the application of linguistic and computational theories to process, interpret, and extract meaningful information from unstructured textual data (Bahdanau, Cho and Bengio, 2015). Researchers and practitioners in NLP employ diverse methodologies, including rule-based approaches, statistical models, machine learning techniques (such as neural networks), and more recently, deep learning architectures. These methodologies enable the development of robust algorithms that can learn from large-scale language data to improve the accuracy and effectiveness of language processing systems (Nilsson, 2010). NLP has numerous real-world applications across various domains, including information retrieval, virtual assistants, chatbots, social media analysis, sentiment monitoring, automated translation services, and healthcare, among others (kaynak). As the field continues to advance, NLP strives to overcome challenges such as understanding the nuances of human language, handling ambiguity, context sensitivity, and incorporating knowledge from diverse sources to enable machines to effectively communicate and interact with humans in a more natural and intuitive manner. Natural Language Processing (NLP) and translation are interconnected fields that share a symbiotic relationship, as NLP techniques and methodologies greatly contribute to the advancement and effectiveness of machine translation systems. NLP, a subfield of artificial intelligence (AI), focuses on the interaction between computers and human language. It encompasses a wide range of tasks, including text analysis, syntactic and semantic parsing, sentiment analysis, information extraction, and machine translation (Bahdanau, Cho and Bengio, 2014). NMT models employ deep learning architectures, such as recurrent neural networks (RNNs) and more specifically, long short-term memory (LSTM) networks, to learn the mapping between source and target language sentences. These models are trained on large-scale parallel corpora, consisting of aligned sentence pairs in different languages. The training process involves optimizing model parameters to minimize the discrepancy between predicted translations and human-generated translations (Wu et al., 2016) NLP techniques are crucial at various stages of machine translation. Preprocessing techniques, such as tokenization, sentence segmentation, and morphological analysis, help break down input text into meaningful linguistic units, making it easier for translation models to process and understand the content. Syntactic and semantic parsing techniques aid in capturing the structural and semantic relationships within sentences, improving the overall coherence and accuracy of translations. Furthermore, NLP-based methods are employed for handling specific translation challenges, such as handling idiomatic expressions, resolving lexical ambiguities, and addressing syntactic divergences between languages. For instance, statistical alignment models, based on NLP algorithms, enable the identification of correspondences between words or phrases in source and target languages, facilitating the generation of more accurate translations (kaynak). Several studies have demonstrated the effectiveness of NLP techniques in enhancing machine translation quality. For example, Bahdanau et al. (2015) introduced the attention mechanism, an NLP technique that enables NMT models to focus on relevant parts of the source sentence during translation. This attention mechanism significantly improved the translation quality of neural machine translation models. ChatGPT is a language model developed by OpenAI that utilizes the principles of Natural Language Processing (NLP) for various tasks, including translations. NLP is a field of artificial intelligence that focuses on the interaction between computers and human language. It encompasses a range of techniques and algorithms for processing, analyzing, and understanding natural language. When it comes to translation, NLP techniques can be applied to facilitate the conversion of text from one language to another. ChatGPT employs a sequence-to-sequence model, a type of neural network architecture commonly used in machine translation tasks. This model takes an input sequence in one language and generates a corresponding output sequence in the target language (OpenAI, 2023). The training process for ChatGPT involves exposing the model to large amounts of multilingual data, allowing it to learn patterns, syntax, and semantic relationships across different languages. This exposure enables the model to develop a general understanding of language structures and meanings, making it capable of performing translation tasks. To enhance translation quality, ChatGPT leverages the Transformer architecture, which has been highly successful in NLP tasks. Transformers utilize attention mechanisms, enabling the model to focus on different parts of the input sequence during the translation process. This attention mechanism allows the model to capture long-range dependencies and improve the overall coherence and accuracy of translations. Additionally, techniques such as subword tokenization, which divides words into smaller units, are commonly employed in NLP translation systems like ChatGPT. Subword tokenization helps handle out-of-vocabulary words and improves the model’s ability to handle rare or unknown words (GPT-4 Technical Report, 2023). As can be seen, there have been significant developments in artificial intelligence translations thanks to NLP. However, it is not possible to say that it has fully reached the quality of translation made by people. The only goal in artificial intelligence translations is to reach translations made by humans. In general, there are some fundamental differences between human and ChatGPT translations. Human-made translations and translations generated by ChatGPT (or similar language models) have several key differences (Kelly and Zetzsche, 2014; Koehn, 2010; Sutskever, Vinyals and Le, 2014; Costa-jussà and Fonollosa, 2018) Translation Quality: Human translators are capable of producing high-quality translations with a deep understanding of both the source and target languages. They can accurately capture the nuances, cultural references, idioms, and context of the original text. On the other hand, ChatGPT translations can sometimes be less accurate or may not fully grasp the intended meaning due to the limitations of the training data and the model’s inability to comprehend context in the same way a human can. While ChatGPT can provide reasonable translations, they may lack the finesse and precision of a human translator. Natural Language Processing: Human translators are skilled at processing and understanding natural language, taking into account the broader context, cultural implications, and the intended audience. They can adapt their translations to suit the target audience, tone, and purpose of the text. ChatGPT, although trained on a vast amount of text data, lacks the same level of natural language understanding. It often relies on pattern matching and statistical analysis to generate translations, which can result in less nuanced or contextually appropriate outputs. Subject Matter Expertise: Human translators often specialize in specific domains or subject areas, allowing them to have deep knowledge and understanding of technical or specialized terminology. They can accurately translate complex or industry-specific texts, ensuring the meaning is preserved. ChatGPT, while having access to a wide range of general knowledge, may struggle with domain-specific vocabulary or terminology, leading to inaccuracies or incorrect translations in specialized texts. Cultural Sensitivity: Human translators are well-versed in the cultural nuances of both the source and target languages. They can navigate potential pitfalls, adapt the translation to the cultural context, and avoid unintended offensive or inappropriate language choices. ChatGPT lacks this level of cultural sensitivity and may produce translations that are culturally tone-deaf or insensitive, as it lacks the ability to understand the subtleties and implications of language choices. Revision and Editing: Human translators go through an iterative process of revision and editing to refine their translations, ensuring accuracy, clarity, and quality. They can self-correct errors and refine their translations based on feedback or additional research. ChatGPT, while capable of generating translations, does not have the same ability to self-correct or improve based on feedback. It generates translations in a single pass, without the iterative refinement process that humans can employ. In summary, while ChatGPT can be a useful tool for generating translations, human-made translations generally outperform machine-generated translations in terms of quality, accuracy, contextuality, cultural sensitivity, and domain-specific expertise. In conclusion, NLP and machine translation are closely intertwined, with NLP providing essential tools, methodologies, and techniques that contribute to the development and improvement of machine translation systems. The integration of NLP methods has led to significant advancements in translation accuracy, fluency, and the ability to handle various linguistic complexities. As NLP continues to evolve, its impact on the field of machine translation is expected to grow, enabling the creation of more sophisticated and context-aware translation systems. On the basis of all this information, in this research, it is aimed to compare the translations from English to Turkish made by ChatGPT, one of the most advanced artificial intelligences, with the translations made by humans. In this context, an academic 1 page English text was chosen. The text was translated by both ChatGPT and a translator who is an academic in the field of translation and has 10 years of experience. Afterwards, two different translations were examined comparatively by 5 different translators who are experts in their fields. Semi-structured in-depth interviews were conducted with these translators. The aim of this study is to reveal the role of artificial intelligence tools in translation, which are increasing day by day and suggesting that there will be no need for language learning in the future. On the other hand, many translators argue that artificial intelligence and human translations can be understood. Therefore, if artificial intelligence is successful, there will be no profession called translator in the future. This research seems to be very useful in terms of shedding light on the future. The method of this research is semi-structured in-depth interview. References Bahdanau, D., Cho, K. and Bengio Y. (2015). Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations. Brown, P. F., Cocke, J., Pietra, S. A. D., Pietra, V. J. D., Jelinek, F., Lafferty, J. D., Mercer, R. L., and Roossin, P. S. A. (1990) statistical approach to machine translation. Computational linguistics 16, 2, 79–85. Costa-jussà, M. R., & Fonollosa, J. A. R. (2018). “An Overview of Neural Machine Translation.” IEEE Transactions on Neural Networks and Learning Systems. GPT-4 Technical Report (2023). https://arxiv.org/abs/2303.08774. Kelly, N. and Zetzsche, J. (2014). Found in Translation: How Language Shapes Our Lives and Transforms the World. USA: Penguin Book. Koehn, P. (2010). “Statistical Machine Translation.” Cambridge University Press. Nilsson, N. J. (2010). The Quest For AI- A History Of Ideas And Achievements. http://ai.standford.edu/ nilsson/. OpenAI (2023). https://openai.com/blog/chatgpt/. Sutskever, I., Vinyals, O., & Le, Q. V. (2014). “Sequence to Sequence Learning with Neural Networks.” Advances in Neural Information Processing Systems. Wu,Y. Schuster, M., Chen, Z., Le, Q. V. and Norouzi M. (2016). Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. https://arxiv.org/pdf/1609.08144.pdf.

## cred ts th s document s an or g nal research. feyza dalaylı conducted and concluded th s research alone. no support was rece ved from any person for the cont nuat on of the research. therefore, as chatgpt emerged and cont nued to develop, the researcher real zed that t was mportant to cons der the subject n terms of translat on. because the bas s of the ncrease n d alogue n the world s the understand ng of nd v duals belong ng to d fferent languages. w th chatgpt, there are many nnovat ons that human ty has ach eved thanks to art f c al ntell gence and technology, wh ch s be ng talked about more and more day by day. th s phenomenon, wh ch was much more d ff cult n the past, today, thanks to art f c al ntell gence, nd v duals can translate any document nto any language they want w th n m nutes. the mportant th ng here s not only the translat on, but the qual ty of the translat on. to date, many tools cont nue to translate on the nternet. however, chatgpt's d fference s ts use of nlp. thus, the translat on s perce ved as more natural and as f t was made by human hands. in th s context, t was deemed appropr ate to conduct a case study n order to better understand the research based on t. for the case study, an nlprelated text n turk sh was translated nto engl sh by both chatgpt and a translator w th 10 years of translat on exper ence. w th n the scope of the case study, these two translat ons, together w th the or g nal text, were shown to 5 academ c an translators who are experts n the r f elds. f rst of all, they were asked wh ch of the translat ons was done by art f c al ntell gence. thus, the d fferences between art f c al ntell gence and human translat ons were tr ed to be determ ned. sem -structured nterv ews were conducted w th the nterv ewees. dur ng the l terature search, both chatgpt and nlp stud es were scanned. however, when t comes to translat on, t has been not ced that the number of stud es that d scuss chatgpt and nlp together s low. the reason for th s s that chatgpt started to be used n a relat vely recent per od. ## introduct on in recent years, the rap d advancements n natural language process ng (nlp) have sparked transformat ve changes across var ous doma ns, nclud ng translat on. the ntegrat on of nlp techn ques nto translat on processes has garnered s gn f cant attent on due to ts potent al to revolut on ze the way we br dge l ngu st c gaps. th s art cle delves nto a case study that explores the ut l zat on of nlp techn ques n translat on, focus ng on a comparat ve evaluat on of translat ons generated by chatgpt, an aipowered language model developed by openai, and those crafted by expert human translators. the study nvest gates the eff cacy and ntr cac es of these translat ons through the lens of f ve d st ngu shed academ c an translators. through a sem -structured nterv ew methodology, we a m to uncover ns ghts nto the strengths, l m tat ons, and nuances assoc ated w th nlp-ass sted translat ons. as nlp technolog es cont nue to evolve, the r appl cat on n translat on has the potent al to streaml ne the process, enhance eff c ency, and expand access to mult l ngual content. however, the challenges posed by d omat c express ons, cultural nuances, and context preservat on rema n focal po nts of concern. th s study seeks to contr bute to the ongo ng d scourse surround ng the ntersect on of nlp and translat on by offer ng a nuanced analys s of translat ons generated by chatgpt n compar son to those crafted by human experts. by delv ng nto the percept ons and observat ons of exper enced academ c translators, t s ntended to shed l ght on the evolv ng landscape of translat on pract ces n the era of art f c al ntell gence dr ven language models. the subsequent sect ons of th s art cle w ll delve nto the methodology employed, the deta ls of the case study, and the ns ghtful f nd ngs der ved from the sem -structured nterv ews w th the academ c an translators. through th s explorat on, t s ntended to prov de a comprehens ve understand ng of the current state, mpl cat ons and poss ble future d rect ons of nlp-ass sted translat on. the ntegrat on of nlp techn ques nto translat on processes holds the prom se of transform ng the translat on landscape, yet t also poses mportant quest ons about the role of human expert se and the preservat on of l ngu st c and cultural subtlet es. th s study bu lds upon th s prem se by exam n ng the pract cal mpl cat ons of nlp-ass sted translat on through the eyes of those deeply entrenched n the f eld. ## nlp and ai natural language process ng (nlp) refers to a f eld of study w th n the doma n of art f c al ntell gence (ai) and computat onal l ngu st cs that focuses on the nteract on between computers and human language. nlp seeks to develop computat onal models and algor thms capable of understand ng, analyz ng, and generat ng natural language text and speech @xcite . natural language process ng (nlp) s a subf eld of art f c al intell gence (ai) that focuses on enabl ng computers to understand, nterpret, and generate human language. the relat onsh p between nlp and ai s symb ot c, as nlp plays a cruc al role n advanc ng the capab l t es of ai systems, wh le ai techn ques contr bute to the development of more soph st cated nlp models. at ts core, nlp a ms to br dge the gap between human language and mach ne understand ng by employ ng var ous techn ques from l ngu st cs, computer sc ence, and stat st cs. it nvolves the appl cat on of l ngu st c and computat onal theor es to process, nterpret, and extract mean ngful nformat on from unstructured textual data @xcite . researchers and pract t oners n nlp employ d verse methodolog es, nclud ng rule-based approaches, stat st cal models, mach ne learn ng techn ques (such as neural networks), and more recently, deep learn ng arch tectures. these methodolog es enable the development of robust algor thms that can learn from large-scale language data to mprove the accuracy and effect veness of language process ng systems @xcite . nlp has numerous real world appl cat ons across var ous doma ns, nclud ng nformat on retr eval, v rtual ass stants, chatbots, soc al med a analys s, sent ment mon tor ng, automated translat on serv ces, and healthcare, among others. as the f eld cont nues to advance, nlp str ves to overcome challenges such as understand ng the nuances of human language, handl ng amb gu ty, context sens t v ty, and ncorporat ng knowledge from d verse sources to enable mach nes to effect vely commun cate and nteract w th humans n a more natural and ntu t ve manner. over t me, nlp has taken place n almost every f eld of l fe and cont nues to take place. because natural language process ng s very successful n convey ng many th ngs about human be ngs. be ng able to nteract w th human-computer, n other words art f c al ntell gence, w th natural language s a s gn f cant step. in th s way, people's work becomes s gn f cantly eas er, as art f c al ntell gence can do what people need to do. on the other hand, when human nteract on w th art f c al ntell gence s done w th natural language process ng, human character st cs can almost be attr buted to art f c al ntell gence. art f c al ntell gence performs a large number of operat ons thanks to natural language process ng. translat on s only one of these processes. dur ng translat on, when art f c al ntell gence s based on natural language process ng, t has many mportant separat on, comb n ng and d st ngu sh ng features. nlp nvolves var ous techn ques and methodolog es that draw from l ngu st cs, computer sc ence, and mach ne learn ng. var ous stud es have dealt w th th s ssue n great deta l. cons der ng these stud es and the r results, some mportant po nts regard ng the relat onsh p between nlp and ai are l sted: @xcite @xcite @xcite @xcite foundat on of ai: language s a fundamental aspect of human commun cat on and ntell gence. develop ng ai systems capable of effect vely understand ng and generat ng human language s a s gn f cant step toward creat ng more humanl ke and capable ai agents. language understand ng: nlp techn ques help ai systems understand the nuances of human language, nclud ng context, semant cs, sent ment, and ntent. th s understand ng s cruc al for tasks such as chatbots, v rtual ass stants, sent ment analys s, and nformat on retr eval. language generat on: ai systems equ pped w th nlp capab l t es can generate coherent and contextually relevant human-l ke language. th s s used n appl cat ons l ke text generat on, content summar zat on, and language translat on. mach ne translat on: ai-powered nlp models have revolut on zed mach ne translat on, enabl ng real-t me translat on of text between languages. th s has far-reach ng mpl cat ons for global commun cat on and collaborat on. sent ment analys s: nlp allows ai systems to analyze and nterpret the sent ment beh nd text data, enabl ng bus nesses to understand customer op n ons, rev ews, and feedback on a large scale. vo ce ass stants: vo ce-based ai ass stants l ke s r , google ass stant, and alexa heav ly rely on nlp to understand spoken language, convert t to text, and execute tasks or prov de nformat on based on user quer es. text class f cat on: nlp techn ques are used for categor z ng and class fy ng text data, wh ch has appl cat ons n spam detect on, content categor zat on, and more. d alog systems: ai-dr ven d alog systems leverage nlp to engage n natural-sound ng conversat ons w th users. th s s used n customer support, v rtual compan ons, and nteract ve systems. challenges: the relat onsh p between nlp and ai also nvolves address ng challenges such as amb gu ty, context, sarcasm, and cultural var at ons n language nterpretat on. bu b lg lerden de anlaşıldığı üzere yapay zeka ve nlp tekn kler b r arada kullanıldığında öneml faydalar sağlamaktadır. bütün bunlar ## nlp, translat on and ai natural language process ng (nlp) and translat on are nterconnected f elds that share a symb ot c relat onsh p, as nlp techn ques and methodolog es greatly contr bute to the advancement and effect veness of mach ne translat on systems. nlp, a subf eld of art f c al ntell gence (ai), focuses on the nteract on between computers and human language. it encompasses a w de range of tasks, nclud ng text analys s, syntact c and semant c pars ng, sent ment analys s, nformat on extract on, and mach ne translat on (bahdanau, @xcite . nmt models employ deep learn ng arch tectures, such as recurrent neural networks (rnns) and more spec f cally, long short term memory (lstm) networks, to learn the mapp ng between source and target language sentences. these models are tra ned on large scale parallel corpora, cons st ng of al gned sentence pa rs n d fferent languages. the tra n ng process nvolves opt m z ng model parameters to m n m ze the d screpancy between pred cted translat ons and human-generated translat ons @xcite nlp techn ques are cruc al at var ous stages of mach ne translat on. preprocess ng techn ques, such as token zat on, sentence segmentat on, and morpholog cal analys s, help break down nput text nto mean ngful l ngu st c un ts, mak ng t eas er for translat on models to process and understand the content. syntact c and semant c pars ng techn ques a d n captur ng the structural and semant c relat onsh ps w th n sentences, mprov ng the overall coherence and accuracy of translat ons. furthermore, nlp-based methods are employed for handl ng spec f c translat on challenges, such as handl ng d omat c express ons, resolv ng lex cal amb gu t es, and address ng syntact c d vergences between languages. for nstance, stat st cal al gnment models, based on nlp algor thms, enable the dent f cat on of correspondences between words or phrases n source and target languages, fac l tat ng the generat on of more accurate translat ons. several stud es have demonstrated the effect veness of nlp techn ques n enhanc ng mach ne translat on qual ty. for example, @xcite ntroduced the attent on mechan sm, an nlp techn que that enables nmt models to focus on relevant parts of the source sentence dur ng translat on. th s attent on mechan sm s gn f cantly mproved the translat on qual ty of neural mach ne translat on models. ## chatgpt, nlp and translat on chatgpt s a language model developed by openai that ut l zes the pr nc ples of natural language process ng (nlp) for var ous tasks, nclud ng translat ons. nlp s a f eld of art f c al ntell gence that focuses on the nteract on between computers and human language. it encompasses a range of techn ques and algor thms for process ng, analyz ng, and understand ng natural language. when t comes to translat on, nlp techn ques can be appl ed to fac l tate the convers on of text from one language to another. chatgpt employs a sequence-to-sequence model, a type of neural network arch tecture commonly used n mach ne translat on tasks. th s model takes an nput sequence n one language and generates a correspond ng output sequence n the target language (openai, 2023). the tra n ng process for chatgpt nvolves expos ng the model to large amounts of mult l ngual data, allow ng t to learn patterns, syntax, and semant c relat onsh ps across d fferent languages. th s exposure enables the model to develop a general understand ng of language structures and mean ngs, mak ng t capable of perform ng translat on tasks. to enhance translat on qual ty, chatgpt leverages the transformer arch tecture, wh ch has been h ghly successful n nlp tasks. transformers ut l ze attent on mechan sms, enabl ng the model to focus on d fferent parts of the nput sequence dur ng the translat on process. th s attent on mechan sm allows the model to capture longrange dependenc es and mprove the overall coherence and accuracy of translat ons. add t onally, techn ques such as subword token zat on, wh ch d v des words nto smaller un ts, are commonly employed n nlp translat on systems l ke chatgpt. subword token zat on helps handle out-of-vocabulary words and mproves the model's ab l ty to handle rare or unknown words (gpt-4 techn cal report, 2023). as can be seen, there have been s gn f cant developments n art f c al ntell gence translat ons thanks to nlp. however, t s not poss ble to say that t has fully reached the qual ty of translat on made by people. the only goal n art f c al ntell gence translat ons s to reach translat ons made by humans. in general, there are some fundamental d fferences between human and chatgpt translat ons. human-made translat ons and translat ons generated by chatgpt (or s m lar language models) have several key d fferences @xcite @xcite translat on qual ty: human translators are capable of produc ng h gh-qual ty translat ons w th a deep understand ng of both the source and target languages. they can accurately capture the nuances, cultural references, d oms, and context of the or g nal text. on the other hand, chatgpt translat ons can somet mes be less accurate or may not fully grasp the ntended mean ng due to the l m tat ons of the tra n ng data and the model's nab l ty to comprehend context n the same way a human can. wh le chatgpt can prov de reasonable translat ons, they may lack the f nesse and prec s on of a human translator. natural language process ng: human translators are sk lled at process ng and understand ng natural language, tak ng nto account the broader context, cultural mpl cat ons, and the ntended aud ence. they can adapt the r translat ons to su t the target aud ence, tone, and purpose of the text. chatgpt, although tra ned on a vast amount of text data, lacks the same level of natural language understand ng. it often rel es on pattern match ng and stat st cal analys s to generate translat ons, wh ch can result n less nuanced or contextually appropr ate outputs. subject matter expert se: human translators often spec al ze n spec f c doma ns or subject areas, allow ng them to have deep knowledge and understand ng of techn cal or spec al zed term nology. they can accurately translate complex or ndustry-spec f c texts, ensur ng the mean ng s preserved. chatgpt, wh le hav ng access to a w de range of general knowledge, may struggle w th doma n-spec f c vocabulary or term nology, lead ng to naccurac es or ncorrect translat ons n spec al zed texts. cultural sens t v ty: human translators are well-versed n the cultural nuances of both the source and target languages. they can nav gate potent al p tfalls, adapt the translat on to the cultural context, and avo d un ntended offens ve or nappropr ate language cho ces. chatgpt lacks th s level of cultural sens t v ty and may produce translat ons that are culturally tone-deaf or nsens t ve, as t lacks the ab l ty to understand the subtlet es and mpl cat ons of language cho ces. rev s on and ed t ng: human translators go through an terat ve process of rev s on and ed t ng to ref ne the r translat ons, ensur ng accuracy, clar ty, and qual ty. they can selfcorrect errors and ref ne the r translat ons based on feedback or add t onal research. chatgpt, wh le capable of generat ng translat ons, does not have the same ab l ty to self-correct or mprove based on feedback. it generates translat ons n a s ngle pass, w thout the terat ve ref nement process that humans can employ. in summary, wh le chatgpt can be a useful tool for generat ng translat ons, human-made translat ons generally outperform mach negenerated translat ons n terms of qual ty, accuracy, contextual ty, cultural sens t v ty, and doma n-spec f c expert se. in conclus on, nlp and mach ne translat on are closely ntertw ned, w th nlp prov d ng essent al tools, methodolog es, and techn ques that contr bute to the development and mprovement of mach ne translat on systems. the ntegrat on of nlp methods has led to s gn f cant advancements n translat on accuracy, fluency, and the ab l ty to handle var ous l ngu st c complex t es. as nlp cont nues to evolve, ts mpact on the f eld of mach ne translat on s expected to grow, enabl ng the creat on of more soph st cated and contextaware translat on systems. ## method the n-depth nterv ew method s a qual tat ve research techn que used to gather deta led and comprehens ve data from part c pants by engag ng them n a structured conversat on. th s method allows researchers to explore complex phenomena, understand nd v duals' perspect ves, and obta n r ch ns ghts nto the r exper ences. in-depth nterv ew method was also used n th s study. in th s context, a text was chosen f rst. th s text has been translated by both chatgpt and a lecturer who s a professor n the f eld of fore gn language and translat on sc ence. then, these two translat on sources were amb guously shown to 5 expert academ c an translators and n-depth nterv ews were conducted on translat ons. ## f nd ngs the f nd ngs of the study are qu te remarkable. f rst of all, 3 of 5 academ c an translators thought that a translator translated the text translated w th chatgpt and stated that the translat on was of h gh qual ty. on the other hand, the rema n ng 2 nterv ewees ns sted that both translat ons were of good qual ty, even f they were aware of the translat on made w th chatgpt. all nterv ewees stated that the reason why chatgpt, wh ch translates v a art f c al ntell gence, s so good s that t uses nlp techn ques correctly and appropr ately. an nterv ewer who not ced the translat on made only w th chatgpt, stated that a few sentences were robot c and cold, and thus he thought that the translat on was not translated by humans. as t can be understood from here, although chatgpt has made s gn f cant progress n translat on, t has not been able to prevent some of ts express ons from be ng cold and far from human s ncer ty. th s shows that t st ll needs to make progress on nlp. in add t on to all these, t s an mportant deta l that only 1 of 5 academ c an translators who have been work ng n the sector and academ c f eld for more than 10 years not ced th s deta l. the nterv ewees also evaluated the future of the relat onsh p between chatgpt and nlp. accord ngly, there s a poss b l ty that the translators' jobs w ll become much eas er n the future, and there may even be t mes when translators are no longer needed. the nterv ewees underl ned that certa n cr ter a should be taken nto account when t comes to translat on. accord ngly, a translat on should nclude a w de var ety of components such as grammar rules, correct and appropr ate use of express ons, naturalness, flu d ty, and the general structure of the translated language. in response to these statements, the nterv ewees were asked to evaluate the d fferences between chatgpt's current status and art f c al ntell gence translat ons n the past. it was conf rmed by all nterv ewees that the development n nlp pract ces was the bas s of these developments. the nterv ewees were asked to express the d fferences between the two translat ons. the ma n purpose here s to dent fy the ma n d fferences between art f c al ntell gence and human translat ons. interest ngly, 3 nterv ewees who thought that art f c al ntell gence translat on was human translat on f rst suggested that the translat on was natural and suff c ent n terms of term nology. from th s po nt of v ew, the level of development of chatgpt n terms of human spontane ty and adequate term nology can be seen. on the other hand, wh le t s an mportant f nd ng that the translat ons made w th chatgpt are not not ced n general, the nterv ewees also put forward the qual ty and smoothness of the translat on as a reason. as t can be understood from here, chatgpt has managed to ncrease the qual ty of translat on n th s sense, as t rece ves nlp support. although the nterv ewees are translators, they support the development of art f c al ntell gence n the future and replac ng people when necessary n translat on. f nally, the nterv ewees were asked to evaluate the use of nlp n art f c al ntell gence supported tools w th n the scope of translat on. except for 1 nterv ewee, all nterv ewees stated that these tools w ll play a pos t ve role n the development of ntercultural commun cat on by br ng ng human ty to an mportant po nt n translat on and fore gn language development n the future. however, one nterv ewee stated that the development of these tools harmed human ty rather than contr buted. accord ng to h m, fore gn language learn ng w ll decrease among people n the future as these tools make people lazy. thanks to nstant translat on, people w ll only commun cate w th the help of tools w thout learn ng anyth ng new. th s w ll cause human ty to move away from naturalness day by day and become robot c. although one nterv ewee expressed her fears about the future w th these statements, the development of chatgpt n translat on s generally apprec ated by the nterv ewees. ## conclus on undoubtedly, the most mportant area where natural language process ng s v s ble to the translator s mach ne translat on. when the development of translat on from the t me t f rst emerged to the present, t cannot be gnored that a very mportant po nt has been reached today. in part cular, the development of art f c al ntell gence tools such as chatgpt s cons dered a turn ng po nt n translat on. chatgpt s capable of render ng qual ty translat ons that are almost nd st ngu shable from human translat ons today. although t st ll needs mprovement, chatgpt has made s gn f cant mprovements n translat on. the bas s of these developments and mprovements s the correct and appropr ate use of nlp techn ques. cons der ng that art f c al ntell gence tools such as chatgpt w ll ncrease n the future, the def c enc es that ex st today w ll be el m nated w th the nlp systems that w ll develop. although there were hopeless and fr ghten ng op n ons about the future among the nterv ewees, n general, th s case study shows that nlp-supported art f c al ntell gence appl cat ons are benef c al for human ty. the translat on done by chatgpt was perce ved by most of the nterv ewees as be ng done by a human translator. th s clearly demonstrates progress. thanks to art f c al ntell gence nlp, t has made a remarkable mprovement n translat on based on the way people th nk and express. as long as chatgpt cont nues to rece ve support from nlp, t w ll be much more successful n the future. accord ng to the general results of the research, nlp enabled the pos t ve development of art f c al ntell gence supported translat on tools. thus, art f c al ntell gence, wh ch can replace human be ngs n the f eld of translat on and allev ate the workload, w ll become even more mportant n the near future. th s s supported by most of the translators. the percept on of translat ons made by art f c al ntell gence such as chatgpt as f they are made by humans s assoc ated w th translat on qual ty, naturalness, subject-verb harmony and general harmony n sentences.