Machine translation (MT) and post-editing (PE) have become increasingly important in the professional language industry in recent years. However, not every translation job is suitable for MT and ...there are many options for carrying out translation/post-editing projects, e.g. no PE, light PE, full PE, full PE plus revision or translation without MT assistance. In 2019, we published a decision tree for post-editing projects (Nitzke et al. 2019) that aimed to take all considerations into account and guide the stakeholders in charge of deciding whether a job is suitable for MT and PE and, if so, what kind of quality assurance might lead to fit-for-purpose translations.
To empirically test our decision tree model now, we developed a semi-structured interview with 21 questions and a scoring task addressing stakeholders who work with MT projects and have to make the decisions which are essential to our model. The interview was carried out with 19 interview partners. In the article, we discuss the interviews’ findings against the background of our model. Further, we present qualitative findings on strategic decisions, risk considerations, as well as the value of translation, working conditions and job profiles. Finally, we present our revised model motivated by the empirical findings.
Machine translation (MT) and post-editing (PE) have become increasingly important in the professional language industry in recent years. However, not every translation job is suitable for MT and ...there are many options for carrying out translation/post-editing projects, e.g. no PE, light PE, full PE, full PE plus revision or translation without MT assistance. In 2019, we published a decision tree for post-editing projects (Nitzke et al. 2019) that aimed to take all considerations into account and guide the stakeholders in charge of deciding whether a job is suitable for MT and PE and, if so, what kind of quality assurance might lead to fit-for-purpose translations. To empirically test our decision tree model now, we developed a semi-structured interview with 21 questions and a scoring task addressing stakeholders who work with MT projects and have to make the decisions which are essential to our model. The interview was carried out with 19 interview partners. In the article, we discuss the interviews’ findings against the background of our model. Further, we present qualitative findings on strategic decisions, risk considerations, as well as the value of translation, working conditions and job profiles. Finally, we present our revised model motivated by the empirical findings.
This paper presents an eyetracking study on language reception in a multilingual society. It focuses on the reading behavior of 23 trilingual participants from South Tyrol, Italy, who speak and ...understand Italian, German and Ladin, the latter a minority language in that area. The eyetracking data show whether all three languages displayed on informational signs are read and processed, and whether processing depends on specific influential factors dependent of the participants or presentation of the language versions. This type of eyetracking application poses several methodological issues which are discussed in the paper. The eyetracking data are triangulated with data from a speech production task and a self-assessment of language skills. The initial results, show that Ladin is preferred over German and Italian in terms of overall fixation duration, that the order of processing follows the marked position top to bottom and left to right, and that participant's language used to describe the processed signs matches that of the language with the most attention per stimulus. Concerning specific language processing strategies initial interpretations are given which must be followed up on.
