This paper explores the influence of integrating the purpose of the translation and the target audience into prompts on the quality of translations produced by ChatGPT. Drawing on previous translation studies, industry practices, and ISO standards, the research underscores the significance of the pre-production phase in the translation process. The study reveals that the inclusion of suitable prompts in large-scale language models like ChatGPT can yield flexible translations, a feat yet to be realized by conventional Ma-chine Translation (MT). The research scrutinizes the changes in translation quality when prompts are used to generate translations that meet specific conditions. The evaluation is conducted from a practicing translator’s viewpoint, both subjectively and qualitatively, supplemented by the use of OpenAI’s word embedding API for cosine similarity calculations. The findings suggest that the integration of the purpose and target audience into prompts can indeed modify the generated translations, generally enhancing the translation quality by industry standards. The study also demonstrates the practical application of the “good translation” concept, particularly in the context of marketing documents and culturally dependent idioms.

In English to Japanese translation, a linear translation refers to a translation in which the word order of the source text is kept as unchanged as possible. Previous research suggests that linear translation reduces the cognitive effort for interpreters and translators compared to the non-linear case. In this study, we empirically tested whether this was also the case in a mon- olingual setting from the viewpoint of reception study. The difference between linear and non-linear translation was defined using Cross values, which quantify how much reordering was required in Japanese translation relative to an English source text. Reading effort was measured by the average total reading time on the target text. In a linear mixed-effects model analysis, variations in reading time per participant and text type were also considered random effects. The results revealed that the reading effort for the linear translation was smaller than that for the non-linear translation. In addition, the accuracy of text comprehension was also found to affect the reading time

This study investigates the impact of translation briefs and search conditions on post-editing (PE) quality produced by participants with different levels of translation proficiency. We hired five Chinese student translators and seven Japanese professional translators to conduct full post-editing (FPE) and light post-editing (LPE), as described in the translation brief, while controlling two search conditions i.e., usage of a termbase (TB) and internet search (IS). Our results show that FPE versions of the final translations tend to have less errors than LPE ver- sions. The FPE translation brief improves participants’ performance on fluency as compared to LPE, whereas the search condition of TB helps to improve participants’ performance on accuracy as compared to IS. Our findings also indicate that the occurrences of fluency errors produced by experienced translators (i.e., the Japanese participants) are more in line with the specifications addressed in translation briefs, whereas the occurrences of accuracy errors pro- duced by inexperienced translators (i.e., our Chinese participants) depend more on the search conditions.

The CRITT (Center for Research and Innovation in Translation and Translation Technology) provides a Translation Process Research Database (TPR-DB) and a rich set of summary tables and tools that help to investigate translator behavior. In this paper, we describe a new tool in the TPR-DB that converts Trados Studio keylogging data (Qualitivity) into Translog-II format and adds the converted data to the CRITT TPR-DB. The tool is also able to synchronize with the output of various eye-trackers. We describe the components of the new TPR-DB tool and highlight some of the features that it produces in the TPR-DB tables.

Speech-enabled interfaces have the potential to become one of the most efficient and ergonomic environments for human-computer interaction and for text production. However, not much research has been carried out to investigate in detail the processes and strategies involved in the different modes of text production. This paper introduces and evaluates a corpus of more than 55 hours of English-to-Japanese user activity data that were collected within the ENJA15 project, in which translators were observed while writing and speaking translations (translation dictation) and during machine translation post-editing. The transcription of the spoken data, keyboard logging and eye-tracking data were recorded with Translog-II, post-processed and integrated into the CRITT Translation Process Research-DB (TPR-DB), which is publicly available under a creative commons license. The paper presents the ENJA15 data as part of a large multilingual Chinese, Danish, German, Hindi and Spanish translation process data collection of more than 760 translation sessions. It compares the ENJA15 data with the other language pairs and reviews some of its particularities.