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This article presents a review of the evolution of automatic post-editing, a term that describes methods to improve the output of machine translation systems, based on knowledge extracted from datasets that include post-edited content. The article describes the specificity of automatic post-editing in comparison with other tasks in machine translation, and it discusses how it may function as a complement to them. Particular detail is given in the article to the five-year period that covers the shared tasks presented in WMT conferences (2015–2019). In this period, discussion of automatic post-editing evolved from the definition of its main parameters to an announced demise, associated with the difficulties in improving output obtained by neural methods, which was then followed by renewed interest. The article debates the role and relevance of automatic post-editing, both as an academic endeavour and as a useful application in commercial workflows.

In recent years, machine translation has made significant advancements; however, its effectiveness can vary widely depending on the language pair. Languages with limited resources, such as Kazakh, Uzbek, Kalmyk, Tatar, and others, often encounter challenges in achieving high-quality machine translations. Kazakh is an agglutinative language with complex morphology, making it a low-resource language. This article addresses the task of post-editing machine translation for the Kazakh language. The research begins by discussing the history and evolution of machine translation and how it has developed to meet the unique needs of languages with limited resources. The research resulted in the development of a machine translation post-editing system. The system utilizes modern machine learning methods, starting with neural machine translation using the BRNN model in the initial post-editing stage. Subsequently, the transformer model is applied to further edit the text. Complex structural and grammatical forms are processed, and abbreviations are replaced. Practical experiments were conducted on various texts: news publications, legislative documents, IT sphere, etc. This article serves as a valuable resource for researchers and practitioners in the field of machine translation, shedding light on effective post-editing strategies to enhance translation quality, particularly in scenarios involving languages with limited resources such as Kazakh and Uzbek. The obtained results were tested and evaluated using specialized metrics—BLEU, TER, and WER.

This article investigates the post-editing workflow and types of edits made in the context of a real-life literary translation project. The source text is a short narrative by Brazilian author Lima Barreto. The text was first machine-translated into German by DeepL and subsequently postedited by a literary translator, using a keylogger to capture edits and intermediate versions.

Human rating of predicted post-editing effort is a common activity and has been used to train confidence estimation models. However, the correlation between human ratings and actual post-editing effort is under-measured. Moreover, the impact of presenting effort indicators in a post-editing user interface on actual post-editing effort has hardly been researched. In this study, ratings of perceived post-editing effort are tested for correlations with actual temporal, technical and cognitive post-editing effort. In addition, the impact on post-editing effort of the presentation of post-editing effort indicators in the user interface is also tested. The language pair involved in this study is English-Brazilian Portuguese. Our findings, based on a small sample, suggest that there is little agreement between raters for predicted post-editing effort and that the correlations between actual post-editing effort and predicted effort are only moderate, and thus an inefficient basis for MT confidence estimation. Moreover, the presentation of post-editing effort indicators in the user interface appears not to impact on actual post-editing effort.

This paper explores the practical prospects for using artificial intelligence technologies in professional English-speaking translator education. At the online conference ‘Translation Skills in Times of Artificial Intelligence’ (DingTalk platform, January 2022), the teachers of higher education institutions in China prioritized the translator’s competencies necessary for successful professional activity during the digital transformation of social and economic business relations. The educators also evaluated the demand for online services used in the education of English–Chinese interpreters. The survey results showed that the use of artificial intelligence technologies in educational practices could have a significant impact on the development of key competencies of future translators. Using a competency-based approach to interpreter training and considering the need to develop abilities, knowledge, and skills required for successful professional translation activity, the author developed the pedagogical concept of the online educational course ‘Simultaneous and asynchronous translation in a digital environment.’

This eye-tracking study compares the post-editing cognitive effort of 25 professionals and 27 students when post-editing NMT and SMT from English to Spanish. Results show no significant differences in post-editing time or fixation duration between groups or MT systems, but reveal reduced fixation duration with NMT for both groups. Aquest estudi de seguiment ocular compara l’esforç cognitiu de post-edició de 25 professionals i 27 estudiants en post-editar la traducció automàtica neuronal (NMT, per les sigles en anglès) i la traducció automàtica estadística (SMT, per les sigles en anglès) de l’anglès al castellà. Els resultats no mostren diferències significatives en el temps de post-edició ni en la durada de les fixacions entre els grups o els sistemes de TA, però revelen una reducció en la durada de les fixacions amb NMT per a tots dos grups. Este estudio de seguimiento ocular compara el esfuerzo cognitivo de post-edición de 25 profesionales y 27 estudiantes al post-editar la traducción automática neuronal (NMT, por sus siglas en inglés) y la traducción automática estadística (SMT, por sus siglas en inglés) del inglés al español. Los resultados no muestran diferencias significativas en el tiempo de post-edición ni en la duración de las fijaciones entre los grupos o los sistemas de TA, pero revelan una reducción en la duración de las fijaciones con NMT para ambos grupos. This eye-tracking study compares the post-editing cognitive effort of 25 professionals and 27 students when post-editing NMT and SMT from English to Spanish. Results show no significant differences in post-editing time or fixation duration between groups or MT systems, but reveal reduced fixation duration with NMT for both groups.

The article explores how AI affects the translation industry in Jordan, the primary concern is whether or not it necessitates the need for human professionals. As transla-tion tools powered by AI become more accurate, widespread and incorporated into processes, the traditional role of translators is confronted in multiple ways. A quanti-tative study based on a structured questionnaire was conducted on the 34 formal registered translation agencies, with a 64% response rate which was intended to measure the opinions of the agencies. The findings of the study indicate that AI has benefited the speed, cost, and quality of the translated output, but has also led to a decrease in the number of human translators by 40 − 70%. Respondents confirm these findings, and also admit that it isn't dependable for texts that are culturally appropriate or have an academic nature, which require human input. The final result of this research is that a new style of hybrid practice is underway in which human translators serve as post-editors to the machine's generated text. These alterations have an effect on the necessity of altering the education, professional training, and the development of ethical principles for the responsible integration of AI in the edu-cational context.

In a translation workflow, machine translation (MT) is almost always followed by a human post-editing step, where the raw MT output is corrected to meet required quality standards. To reduce the number of errors human translators need to correct, automatic post-editing (APE) methods have been developed and deployed in such workflows. With the advances in deep learning, neural APE (NPE) systems have outranked more traditional, statistical, ones. However, the plethora of options, variables and settings, as well as the relation between NPE performance and train/test data makes it difficult to select the most suitable approach for a given use case. In this article, we systematically analyse these different parameters with respect to NPE performance. We build an NPE “roadmap” to trace the different decision points and train a set of systems selecting different options through the roadmap. We also propose a novel approach for APE with data augmentation. We then analyse the performance of 15 of these systems and identify the best ones. In fact, the best systems are the ones that follow the newly-proposed method. The work presented in this article follows from a collaborative project between Microsoft and the ADAPT centre. The data provided by Microsoft originates from phrase-based statistical MT (PBSMT) systems employed in production. All tested NPE systems significantly increase the translation quality, proving the effectiveness of neural post-editing in the context of a commercial translation workflow that leverages PBSMT.

This study aims to analyse how translation experts from the German department of the European Commission’s Directorate-General for Translation (DGT) identify and correct different error categories in neural machine translated texts (NMT) and their post-edited versions (NMTPE). The term translation expert encompasses translator, post-editor as well as revisor. Even though we focus on neural machine-translated segments, translator and post-editor are used synonymously because of the combined workflow using CAT-Tools as well as machine translation. Only the distinction between post-editor, which refers to a DGT translation expert correcting the neural machine translation output, and revisor, which refers to a DGT translation expert correcting the post-edited version of the neural machine translation output, is important and made clear whenever relevant. Using an automatic error annotation tool and the more fine-grained manual error annotation framework to identify characteristic error categories in the DGT texts, a corpus analysis revealed that quality assurance measures by post-editors and revisors of the DGT are most often necessary for lexical errors. More specifically, the corpus analysis showed that, if post-editors correct mistranslations, terminology or stylistic errors in an NMT sentence, revisors are likely to correct the same error type in the same post-edited sentence, suggesting that the DGT experts were being primed by the NMT output. Subsequently, we designed a controlled eye-tracking and key-logging experiment to compare participants’ eye movements for test sentences containing the three identified error categories (mistranslations, terminology or stylistic errors) and for control sentences without errors. We examined the three error types’ effect on early (first fixation durations, first pass durations) and late eye movement measures (e.g., total reading time and regression path durations). Linear mixed-effects regression models predict what kind of behaviour of the DGT experts is associated with the correction of different error types during the post-editing process.

This article investigates the post-editing workflow and types of edits made in the context of a real-life literary translation project. The source text is a short narrative by Brazilian author Lima Barreto. The text was first machine-translated into German by DeepL and subsequently postedited by a literary translator, using a keylogger to capture edits and intermediate versions.