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This study explores the translation of the most error-prone legal terms in the translation of statutes between Chinese and Korean. Korean and Chinese belong to the same Chinese character culture, so they share common features that facilitate translations between the two languages, but the same features may cause many errors in translation. There are many Chinese characters that cannot be translated on a one-on-one basis due to legal system and cultural differences, and there are many similar words that are very limited in their use due to their legal meanings. In addition, there are gaps in the legal systems between the two countries, so this study attempts to look into how to translate these legal terms. The study will contribute to the development of reference materials and guidelines for educating or translating legal terms.

This study compares the mediation aspects of three interpreters at a cross-examination in criminal trial with the aim of observing how the different educational backgrounds of the interpreters affect the proceedings in trial. Judicial interpretation education programs were offered within the nation in a variety of ways due to awareness of the importance of judicial interpretation. Among the three interpreters, interpreter 1 and interpreter 2 had completed the judicial interpretation education program while interpreter 3 had not taken part in the said education program. The study conducts a discourse analysis to measure the mediation aspects of three interpreters during court interpreting. First, the number of questions asked are quantitatively analyzed to measure the intervention of the interpreter. Then factors such as ‘additional questions’, ‘use of third person’, ‘explanation’, ‘communicative coordination’ are considered for inductive deduction in order to measure the mediation aspect of the interpreter. As a result, it was confirmed that interpreter 3 was giving too many additional questions, thereby affecting the proceeding of the trial.

Post-editing of machine translation has gained public attention thanks to its arguably high productivity and reasonable quality of the product. While the use of machine translation and post-editing increased, there have not been adequate assessment and deliberations on the productivity and quality of English to Korean post-editing. This study attempts to compare the human translation and post-editing products and assess their productivity. The analysis shows that the productivity of post-editing is higher than that of human translation in general. However, participants showed significant differences in the level of productivity gain, and some participants achieved high productivity at the cost of quality. Furthermore, participants showed different error correction behaviors when the text becomes more difficult. In this regard, it is reasonable to argue that English to Korean post-editing can help enhance translation productivity, but it is advisable to consider the factors that may negatively affect productivity and quality.

Collocations are lexical or grammatical combinations of preferred words in certain uses. In corpus linguistics, collocations can be explored using several methods such as co-occurrences and the n-gram, which allow the lexical distributions around a keyword in a big corpus to be closely observed. Corpus-based translation studies also use these methods to find out the translation correspondences of collocations between two different languages. This article aims to observe co-occurrences between French and Korean and to find their translation equivalences in a Web as Corpus concerning the coronavirus. After building a coronavirus-centered French-Korean corpus from the web, we examined co-occurrences of some keywords like coronavirus/‘코로나 바이러스’, cas/‘확진자’, and vaccin/‘백신’. This method allowed us to carefully examine co-occurring words between French and Korean and to investigate their translation characteristics. In addition, we verified the usefulness of the Web as Corpus in this type of study with regard to specific subjects.

The aim of this paper is to explore some of the resources of written language (as opposed to spoken language) in English-Korean translation with a view to assisting trainee translators in delivering ‘translationese’-free natural translation in different types of texts. Trainee translators in their transition from the ‘general language (GL)’ translation phase to the ‘special language (SL)’ one (terms used following Snell-Hornby, 1990: 33) tend to confuse translation strategies for the two different phases. Consequently, they may inadvertently apply the translation skills they have developed in the former to the latter, often producing ‘awkward’ translation. In English-Korean translation, this is particularly true in terms of how much written-ness their translated text should involve in comparison with translations rendered by professional translators. In this paper, written-ness is measured using the concept of ‘grammatical metaphor’ whose central contributor is nominalisation (Halliday, 2004), along with two other criteria: number of clauses per sentence and lexical density. The main data of this pilot study includes two GL texts and two SL texts and their translations by trainees (TT1) and professional translators (TT2). The results of the analysis indicate that TT1 of GL texts should be more spoken than as is while that of SL texts needs to be considerably more written.

This study aims to develop machine translation quality measures by statistically verifying the factor structure of translation quality perceived by professional translators. The measurement variables observed in the analysis are indicators that describe concepts widely used in translation studies (i.e.,fluency, readability, accuracy, fidelity, cohesion, and functionality). This study first established a theoretical factor structure, conducted an explanatory factor analysis to extract commonality-based factors, compared the theoretical factor structure and the statistically extracted one, and then performed a confirmatory factor analysis to verify the validity of the machine translation measures developed in the derived model based on structural equation modeling (SEM). As a result, the model fit (RMSEA = .072, CFI = .922) was evaluated very high at 92.2%.