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Posted on by Michael Farrell Posted in Academic publications

Abstract

Michael Farrell introduces “synthetic-text editing,” a new profession emerging alongside translation. Unlike machine translation post-editing, this involves revising generative AI output, which often displays redundancy, flat rhythm, contradictions and English-influenced patterns in other languages. Experiments revealed that both professional translators and students struggled to distinguish AI from human writing, while some readers even preferred AI-generated texts. Still, linguistic expertise is essential to correct subtle errors and cultural blunders. Farrell argues that synthetic-text editing will be vital in high-stakes domains like law, medicine and marketing, where precision, authenticity and human tone remain indispensable despite AI’s growing role.

Published in

ITI Bulletin, October 2025; pp. 13-14.

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Posted on by Michael Farrell Posted in Academic publications

Abstract

In this insightful article, published in the ITI Bulletin in July 2025, Michael Farrell explores the limitations of generative AI (GenAI) in handling obscure or technical terminology, which is a common challenge for professional translators. He proposes Retrieval-Augmented Generation (RAG) as a promising solution. RAG enhances GenAI by integrating external data sources, thereby improving factual accuracy. Farrell illustrates its potential through examples from translation workflows and highlights RAG’s role in empowering translators as informed decision-makers.

Published in

ITI Bulletin, July 2025; pp. 27-29.

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Posted on by Michael Farrell Posted in Academic publications

Abstract

This article reports professional translators’ attitudes toward generative artificial intelligence (GenAI) tools. Based on an anonymous online survey of 425 respondents conducted in early 2024, the research reveals a profession divided: while 29.4% of translators have integrated GenAI into their workflow, mainly for writing and text refinement, most remain sceptical. Concerns include quality, privacy, ethics and the potential erosion of professional standards. ChatGPT emerged as the dominant GenAI tool, yet only a minority of users disclose its use to clients. The findings suggest that while GenAI is being cautiously adopted as an additional tool, it is far from replacing traditional machine translation methods.

Published in

In Touch, July, Winter 2025; p. 17.

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Posted on by Michael Farrell Posted in Academic publications

Abstract

Given the growing use of generative artificial intelligence as a tool for creating multilingual content and bypassing both machine and traditional translation methods, this study explores the ability of linguistically trained individuals to discern machine-generated output from human-written text (HT). After brief training sessions on the textual anomalies typically found in synthetic text (ST), twenty-three postgraduate translation students analysed excerpts of Italian prose and assigned likelihood scores to indicate whether they believed they were human-written or AI-generated (ChatGPT-4o). The results show that, on average, the students struggled to distinguish between HT and ST, with only two participants achieving notable accuracy. Closer analysis revealed that the students often identified the same textual anomalies in both HT and ST, although features such as low burstiness and self-contradiction were more frequently associated with ST. These findings suggest the need for improvements in the preparatory training. Moreover, the study raises questions about the necessity of editing synthetic text to make it sound more human-like and recommends further research to determine whether AI-generated text is already sufficiently natural-sounding not to require further refinement.

Published in

Machine Translation Summit XX: proceedings, volume 1. 23-27 June 2025; pp. 432-441.

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Posted on by Michael Farrell Posted in Academic publications

Abstract

As the use of generative artificial intelligence (GenAI) becomes more mainstream, an increasing number of authors may turn to this technology to write directly in a second language, bypassing traditional translation methods. Consequently, professional editors may have to develop new skills: shifting from correcting translation and non-native errors to editing AI-assisted texts. This study includes several stages: participant selection, text planning, prompt engineering, text generation and text editing. The recruited authors provided prompts for GPT-4 to generate texts, edited the output as they desired and then passed them on to professional editors for a final edit. All participants reported their experiences and described the nature of their interactions. The findings reveal that, while GenAI significantly improved the grammatical accuracy of the non-native English texts, it also introduced anomalies. In conclusion, although AI was useful in these two cases, it did not fully replace the human editors, and professional translators-with their language skills-may like to consider offering this additional service. The study also suggests that both authors and editors should be trained in synthetic-text editing to fully harness the benefits of AI-assisted writing, and that further research should be conducted with diverse texts and authors to generalize the findings.

Published in

Translating and the Computer 46: proceedings. Asling: International Society for Advancement in Language Technology, 18-20 November 2024; pp. 35‑46 (ISBN 978-2-9701733-2-8).

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Posted on by Michael Farrell Posted in Academic publications

Abstract

This paper presents the findings of an anonymous online survey conducted in early 2024 on the use of generative artificial intelligence (GenAI) among professional translators. The survey revealed that 29.4% of professional translators incorporate GenAI into their workflow, in line with the results of another recent study. There is a significant association between the use of machine translation (MT) and GenAI, with MT users more likely to also use GenAI. Translators primarily use GenAI for writing-related tasks, such as finding contextual meanings, rephrasing sentences, shortening, summarizing and simplifying, and finding metaphors, synonyms and definitions. This suggests that GenAI enhances translation quality rather than productivity. Only 28.8% of GenAI users use it more than 50% of the time, implying that it is just one of several tools. ChatGPT is the most popular GenAI system, used by 80.8% of GenAI users, followed by Microsoft Copilot at 29.6%. However, only 20% of GenAI users pay for premium services. Many professional translators do not use GenAI (70.6%), often due to strong negative attitudes. GenAI’s role as an alternative to traditional MT followed by post-editing is less common than might be expected.

Published in

Translating and the Computer 46: proceedings. Asling: International Society for Advancement in Language Technology, 18-20 November 2024; pp. 23‑34 (ISBN 978-2-9701733-2-8).

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Posted on by Michael Farrell Posted in Academic publications

Abstract

In this follow-up to his previous piece on machine-like thinking, Michael Farrell explores the enduring gap between machine translation and human understanding. Using linguistic puzzles and historical context, he revisits Yehoshua Bar-Hillel’s argument that machines, lacking real-world knowledge, cannot fully replicate human translation. Farrell demonstrates that while AI systems can mimic co-textual patterns, they fail to grasp cultural and contextual subtleties, such as film title localization. He argues that generative AI, limited by probabilistic reasoning and unaligned data, cannot replace human inference and critical judgment. The article ultimately reaffirms the indispensable role of human translators in a GenAI-driven world.

Published in

ITI Bulletin, July-August 2024

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Posted on by Michael Farrell Posted in Academic publications

Abstract

In this thought-provoking article, published in the ITI Bulletin in May 2024, Michael Farrell explores whether understanding is truly necessary for accurate translation. Through two carefully designed linguistic puzzles, he demonstrates how machines, unlike humans, rely purely on pattern recognition, statistical inference and token embeddings to translate between languages without grasping meaning. Drawing on natural language processing concepts and the principle that “you shall know a word by the company it keeps,” Farrell highlights both the power and the limitations of machine translation and generative AI. The article sets the stage for a follow-up discussion on why human insight remains essential in the translation process, which appears in the following edition of the ITI Bulletin in July 2024.

Published in

ITI Bulletin, May-June 2024

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Posted on by Michael Farrell Posted in Academic publications

Abstract

This article by Michael Farrell, published in the ITI Bulletin in September 2023, critically explores the evolving landscape of machine translation post-editing (MTPE), examining its practicality, productivity and psychological impact on professional translators. Beginning with a historical overview of machine translation (MT), Farrell highlights the initial shortcomings of early systems and the long-standing scepticism surrounding fully automatic high-quality translation. The article then delves into the transformative role of neural MT (NMT) and large language models (LLMs), which have improved raw MT output quality, prompting a rise in MTPE adoption. However, despite potential productivity gains, many translators still reject MTPE due to factors such as text type suitability, lack of job satisfaction and the limitations of MT-generated vocabulary. Farrell also discusses issues like post-editese and the challenges of monolingual post-editing. Ultimately, he argues that while MTPE may become more prevalent, it should be approached as a tool rather than a replacement, emphasizing the enduring value of human translators in ensuring nuanced, high-quality translations.

Published in

ITI Bulletin, September-October 2023

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Posted on by Michael Farrell Posted in Academic publications

Abstract

The experiment reported in this paper is a follow-up to one conducted in 2017/2018. The new experiment aimed to establish if the previously observed lexical impoverishment in machine translation post-editing (MTPE) has become more marked as technology has developed or if it has attenuated. This was done by focusing on two n-grams, which had been previously identified as MT markers, i.e., n-grams that give rise to translation solutions that occur with a higher frequency in MTPE than is natural in HT. The new findings suggest that lexical impoverishment in the two short texts examined has indeed diminished with DeepL Translator. The new experiment also considered possible syntactic differences, namely the number of text segments in the target text. However no significant difference waThe experiment reported in this paper is a follow-up to one conducted in 2017/2018. The new experiment aimed to establish if the previously observed lexical impoverishment in machine translation post-editing (MTPE) has become more marked as technology has developed or if it has attenuated. This was done by focusing on two n-grams, which had been previously identified as MT markers, i.e., n-grams that give rise to translation solutions that occur with a higher frequency in MTPE than is natural in HT. The new findings suggest that lexical impoverishment in the two short texts examined has indeed diminished with DeepL Translator. The new experiment also considered possible syntactic differences, namely the number of text segments in the target text. However no significant difference was observed. The participants were asked to complete a short questionnaire on how they went about their tasks. It emerged that it was helpful to consult the source language text while post-editing, and the original unedited raw output while self-revising, suggesting that monolingual MTPE of the two chosen texts would have been unwise. Despite not being given specific guidelines, the productivity of the post-editors increased. If the ISO 18587:2017 recommendation of using as much of the MT output as possible had been strictly followed, the MTPE would have been easier to distinguish from HT. If this can be taken to be generally true, it suggests that it is neither necessary nor advisable to follow this recommendation when lexical diversity is crucial for making the translation more engaging.

Published in

International Conference on Human-Informed Translation and Interpreting Technology (HiT-IT 2023): proceedings. Naples, Italy, 7-9 July 2023.

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