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.
Technologies offered and used on the Internet play a significant part in the lives of children; nevertheless, little research has been done on how children view and use machine translation (MT). According to recent literature, there are various benefits to using MT in teaching/learning foreign languages, such as more fluent writing, more effective communication, and fewer errors. Nevertheless, the use of MT in classroom settings is often viewed as problematic by language teachers. Despite the fact that a vast number of students have used MT for various purposes or have tried experimenting with MT for certain academic or entertainment purposes, they seem to have mixed feelings about it. The present qualitative study is based on semi-structured interviews and aims to capture a snapshot of Lithuanian children’s perceptions and awareness of MT technologies. The results of the interviews reveal that children mostly find out about MT as a result of their own efforts and employ MT tools for a variety of purposes; however, at school no systematic guidance and/or support in terms of MT use is provided and children tend to perceive that their teachers generally hold negative attitudes towards MT.
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