Translation and Translanguaging in Multilingual Contexts - Volume 11, Issue 1, 2025

Abstract

The acquisition of knowledge is essential for specialized translation, and the representation of specialized phraseology in terminological knowledge bases facilitates this process. The aim of this study is two-fold. Firstly, it describes how the semantic annotation of the predicate-argument structure of sentences mentioning named rivers can be addressed from the perspective of Frame-based Terminology. The results show that this approach, including the semantic variables of verb lexical domain, semantic role, and semantic category, provides valuable insights into the knowledge structures underlying the usage of named rivers in specialized texts. Secondly, this study explores whether the bracketing of a three-component multiword term can be predicted from the semantic information encoded in the sentence where the ternary compound and a named river are used as arguments. The semantic variables of lexical domain, semantic role, and semantic category allowed us to construct two machine-learning models capable of accurately predicting ternary-compound bracketing.

Abstract

Machine translation (MT) and post-editing (PE) have become increasingly common in translation training (Guerberof-Arenas and Moorkens 2019; Sánchez Ramos 2022). However, few studies have explored how trainee translators’ emotional responses to MT and PE impact their motivation to incorporate these tools in their translation practice. Understanding and managing the emotions of students is crucial to integrating MT and PE within translator training as a whole, and to fully preparing students for their professional careers. Building on previous studies applying emotional narrative analysis to translators’ experiences with technology (Koskinen and Ruokonen 2017; Ruokonen and Koskinen 2017), this article explores the emotional responses of a group of 35 Spanish undergraduate translation students to MT and PE using an emotional narrative methodology. Participants were asked to write an emotional narrative in the form of either a love letter or a break-up letter addressed to MT. Overall, participants were found to be more positive than negative in their attitude towards MT and PE. Among the 35 emotional narratives, there were 20 love letters and 15 break-up letters. Thematic analysis revealed two main themes in the students’ narratives relating to (1) satisfaction with MT output and (2) the efficiency of MT. These exploratory findings reinforce Yang and Wang’s (2019) call for more research into what factors contribute to students’ intention to use MT and the consequent effects of using MT. They can also inform pedagogy so that translator trainers better understand the factors that motivate students to incorporate MT in their translation practice.

Abstract

Machine translation (MT) has surpassed all quality expectations and its use has increased exponentially in recent years (Forcada 2017; Sánchez-Gijón, Moorkens, and Way 2019). One of the most frequent MT applications is the translation of user-generated content (UGC) and, more specifically, reviews on tourism portals such as Tripadvisor. Several authors agree that the degree of trust and credibility of a review, as the most important characteristics of UGC, depends largely on the perceived naturalness and authenticity of its writing (Pollach 2006; Schemmann 2011; Vásquez 2014). The review’s influence on the product’s reputation and on the purchase decision-making of future users has been fully demonstrated. Since review platforms make reviews available to users in different languages translated by MT, the quality of MT output should be studied from the point of view of the text’s adaptation to the requirements of a specific audience and market, following the principles elaborated in localization studies. The aim of this paper is to analyze the behavior of neural MT of user-generated content from the perspective of localization to check whether MT quality depends exclusively on linguistic or stylistic aspects or whether the aspects studied by localization, such as linguistic and cultural appropriateness for the target user, also play a decisive role. We compiled an English-Spanish parallel corpus consisting of 250 reviews retrieved from Tripadvisor. The reviews were originally written in English and MT processed into Spanish. Then the quality of the MT output was evaluated following two parameters: correctness and acceptance according to MT quality scales and localization guidelines.

Abstract

This paper analyses the effectiveness of neural machine translation when applied to literary translation and, more specifically, to the translation of collocations, one of the most difficult aspects in machine translation (Corpas-Pastor 2015; Shraiden and Mahadin 2015). Literary translation continues to constitute one of the biggest challenges for machine translation (Toral and Way 2018), where cohesion errors are amongst the most frequent (Voigt and Jurafsky 2012). A comparative analysis of the translation of the first chapter of the world literature masterpiece El ingenioso hidalgo don Quijote de la Mancha — known as Don Quixote in English — was carried out, paying close attention to collocations. The human translation done by Tom Lathrop (Don Quixote) was compared to the target texts obtained with the two biggest neural machine translation systems today, Google Translate and DeepL, to see which provided more accurate results. The results confirm that neural machine translation offers highly reliable results. On a quantitative level the margins are very narrow when determining which system, DeepL or Google Translate, is better. DeepL scored better in terms of accuracy and recall, but in the BLEU metrics Google Translate scored 28.10 and DeepL 26.63. On a qualitative level and from a subjective point of view, we found DeepL’s translation to be somewhat more fluid and natural than Google Translate’s.

Abstract

As language teaching becomes more complex and diverse, there has been a rapid increase in the demand for advanced technology, driving the widespread adoption of neural machine translation (NMT) and ChatGPT in the field. This study contributes to the literature on the use of technology in language teaching by evaluating the application of NMT technology and ChatGPT in teaching English as a foreign language (EFL) writing in three business fields: finance, economics, and business administration. By building six comparable corpora consisting of students’ direct-writing and post-edited writing based on machine-translated texts, we examined whether NMT can help improve students’ performance in business English writing classes, and whether ChatGPT can complement NMT. Our statistical analyses show that in general, NMT can enhance the proficiency of students’ academic writing, but its improvement effect works on different dimensions for those students studying in different majors. Specifically, for finance students, NMT can improve their academic writing at the word and syntax levels and mechanics, while it harms the organizational dimension. For students in economics, the improvement effect of NMT mainly focuses on enhancing the dimensions of syntax, cohesion, and mechanics, whereas for students in business administration, NMT works primarily on the dimensions of content, cohesion, and mechanics. As for the dimensions where NMT performs poorly, our analysis of students’ essay writing shows that ChatGPT can complement NMT by making improvements and providing feedback to students. Our paper adds value to existing research on the use of technology in language teaching by investigating the application of NMT and ChatGPT in teaching EFL writing, and by proposing potential directions for their use in the teaching of writing business English.

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