Artificial intelligence-grounded machine translation has fundamentally changed public awareness and attitudes towards multilingual communication. In some language pairs, the accuracy, quality and efficiency of machine-translated texts of certain types can be quite high. Hence, the end-user acceptability and reliance on machine-translated content could be justified. However, machine translation in small and/or low-resource languages might yield significantly lower quality, which in turn may lead to potentially negative consequences and risks if machine translation is used in high-risk contexts without awareness of the drawbacks, critical assessment and modifications to the raw output. The current study, which is part of a more extensive project focusing on the societal impact of machine translation, is aimed at revealing the attitudes towards usability and quality as perceived from the end-user perspective. The research questions addressed revolve around the machine translation types used, purposes of using machine translation, perceived quality of the generated output, and actions taken to improve the quality by users with various backgrounds. The research findings rely on a survey of the population (N = 402) conducted in 2021 in Lithuania. The study reveals the frequent use of machine translation for a diversity of purposes. The most common uses include work, research and studies, and household environments. A higher level of education correlates with user dissatisfaction with the generated quality and actions taken to improve it. The findings also reveal that age correlates with the use of machine translation. Sustainable measures to reduce machine translation related risks have to be established based on the perceptions of different social groups in different societies and cultures.

Despite fast development of machine translation, the output quality is less than acceptable in certain language pairs. The aim of this paper is to determine the types of errors in machine translation output that cause comprehension problems to potential readers. The study is based on a reading task experiment using eye tracking and a retrospective survey as a complementary method to add more value to the research as eye tracking as a method is considered to be problematic and challenging (O’BRIEN, 2009; ALVES et al., 2009). The cognitive evaluation approach is used in an eye tracking experiment to determine the complexity of the errors in the English–Lithuanian language pair from easiest to hardest as seen by the readers of a machine-translated text. The tested parameters – gaze time and fixation count – demonstrate that a different amount of cognitive effort is required to process different types of errors in machine-translated texts. The current work aims at contributing to other research in the Translation Studies field by providing the analysis of error assessment of machine translation output.

Machine translation (MT) is still a huge challenge for both IT developers and users. From the beginning of machine translation, problems at the syntactic and semantic levels have been faced. Today despite progress in the development of MT, its systems still fail to recognise which synonym, collocation or word meaning should be used. Although mobile apps are very popular among users, errors in their translation output create misunderstandings. The paper deals with the analysis of machine translation of general everyday language in Lithuanian to English and English to Lithuanian language pairs. The results of the analysis show that more than two thirds of all the sentences were translated incorrectly, which means that there is a relatively small possibility that a mobile app will translate sentences correctly. The results are disappointing, because even after almost 70 years of MT research and improvement, researchers still cannot offer a system that would be able to translate with at least 50% correctness.

The paper presents a cross-European survey on teachers and crowdsourcing. The survey examines how familiar language teachers are with the concept of crowdsourcing and addresses their attitude towards including crowdsourcing into language...

Eye-tracking methods have become a valuable tool for reading research, as they provide significant evidence on the behavioral and cognitive performance of developing readers. They also help identify reading behavior patterns among children with neurodevelopmental conditions, such as reading difficulties (RD) or attention-deficit hyperactivity disorder (ADHD). In this paper, we reviewed relevant literature (73 empirical studies) from 2008 to 2023 (15 years). We documented how reading performance was measured through eye-tracking methodology among typical and atypical readers across various languages. To ensure a rigorous and transparent review, we adhered to the PRISMA framework, defined keywords, established inclusion and exclusion criteria, conducted data extraction, and employed a multi-phase selection process. We focused on research samples, stimulus designs, independent variables, eye-tracking systems, and metrics. The results indicated that research involving typically developing children mainly included participants from primary school and employed various eye-tracking methods, with English being the most studied language. Studies on children with RD and ADHD varied significantly in participants’ sample sizes, languages, and experimental designs. The discussion emphasizes the need for standardized data-collection procedures to enhance the reliability and comparability of findings, thereby supporting the development of effective interventions and instructional methods for reading difficulties.