In both East and West Germany, Wisława Szymborska was discovered early and published in translations by various translators in numerous journals and anthologies. By 1990, the year of reunification, three volumes of her poems were published in West Germany, translated and edited by Karl Dedecius, whereas in East Germany only one book was published, edited and translated by Jutta Janke. This article offers an analysis of these publications in both German states, focussing on which poems were included in which publications and which poems were not published in either state. Finally, one of the poems, translated and published in both German states, “Dwie małpy Bruegla” [“Brueghel’s two monkeys”], is compared using the so-called Göttingen approach to translation research. This methodological approach assumes that the differences between the source text and the target text can provide indications of the conditions under which the respective translations were written, in order to find out to what extent the translations differ in the Federal Republic of Germany and the German Democratic Republic. Although individual poems might have been chosen for ideological reasons, the assumption that differences in translations of the same poem could be due to ideological factors cannot be confirmed in the case of this particular poem.
Michael Carl, Takanori Mizowaki, Aishvarya Raj
et al.
Building on the Extended Mind (EM) theory and radical enactivism, this article suggests an alternative to representation-based models of the mind. We lay out a novel ABC framework of the translating mind, in which translation is not the manipulation of static interlingual correspondences but an enacted activity, dynamically integrating affective, behavioral, and cognitive (ABC) processes. Drawing on Predictive Processing and (En)Active Inference, we argue that the translator's mind emerges, rather than being merely extended, through loops of brain-body-environment interactions. This non-representational account reframes translation as skillful participation in sociocultural practice, where meaning is co-created in real time through embodied interaction with texts, tools, and contexts.
LLMs have paved the way for truly simple document-level machine translation, but challenges such as omission errors remain. In this paper, we study a simple method for handling document-level machine translation, by leveraging previous contexts in a multi-turn conversational manner. Specifically, by decomposing documents into segments and iteratively translating them while maintaining previous turns, this method ensures coherent translations without additional training, and can fully re-use the KV cache of previous turns thus minimizing computational overhead. We further propose a `source-primed' method that first provides the whole source document before multi-turn translation. We empirically show this multi-turn method outperforms both translating entire documents in a single turn and translating each segment independently according to multiple automatic metrics in representative LLMs, establishing a strong baseline for document-level translation using LLMs.
Aims and objectives To describe the importance of, and methods for, successfully conducting and translating research into clinical practice. Background There is universal acknowledgement that the clinical care provided to individuals should be informed on the best available evidence. Knowledge and evidence derived from robust scholarly methods should drive our clinical practice, decisions and change to improve the way we deliver care. Translating research evidence to clinical practice is essential to safe, transparent, effective and efficient healthcare provision and meeting the expectations of patients, families and society. Despite its importance, translating research into clinical practice is challenging. There are more nurses in the frontline of health care than any other healthcare profession. As such, nurse‐led research is increasingly recognised as a critical pathway to practical and effective ways of improving patient outcomes. However, there are well‐established barriers to the conduct and translation of research evidence into practice. Design This clinical practice discussion paper interprets the knowledge translation literature for clinicians interested in translating research into practice. Methods This paper is informed by the scientific literature around knowledge translation, implementation science and clinician behaviour change, and presented from the nurse clinician perspective. We provide practical, evidence‐informed suggestions to overcome the barriers and facilitate enablers of knowledge translation. Examples of nurse‐led research incorporating the principles of knowledge translation in their study design that have resulted in improvements in patient outcomes are presented in conjunction with supporting evidence. Conclusions Translation should be considered in research design, including the end users and an evaluation of the research implementation. The success of research implementation in health care is dependent on clinician/consumer behaviour change and it is critical that implementation strategy includes this. Relevance to practice Translating best research evidence can make for a more transparent and sustainable healthcare service, to which nurses are central.
Sugerimos un marco teórico-práctico para el análisis de textos (traducidos) desde una perspectiva feminista; un modelo integrador que el alumnado pueda aplicar en asignaturas afines de grado y postgrado, colaborando de esta forma a la incorporación de la perspectiva de género en la docencia de la traducción. Sugeriremos una base teórica que parte de los estudios de género y traducción para destacar conceptos clave para un análisis traductológico con perspectiva feminista, tales como el estudio de la re-traducción, la recepción, las estrategias de traducción (feministas) textuales y paratextuales, así como de los agentes implicados en el proceso de traducción.
Ali TehraniJamsaz, Arijit Bhattacharjee, Le Chen
et al.
Recent advancements in Large Language Models (LLMs) have renewed interest in automatic programming language translation. Encoder-decoder transformer models, in particular, have shown promise in translating between different programming languages. However, translating between a language and its high-performance computing (HPC) extensions remains underexplored due to challenges such as complex parallel semantics. In this paper, we introduce CodeRosetta, an encoder-decoder transformer model designed specifically for translating between programming languages and their HPC extensions. CodeRosetta is evaluated on C++ to CUDA and Fortran to C++ translation tasks. It uses a customized learning framework with tailored pretraining and training objectives to effectively capture both code semantics and parallel structural nuances, enabling bidirectional translation. Our results show that CodeRosetta outperforms state-of-the-art baselines in C++ to CUDA translation by 2.9 BLEU and 1.72 CodeBLEU points while improving compilation accuracy by 6.05%. Compared to general closed-source LLMs, our method improves C++ to CUDA translation by 22.08 BLEU and 14.39 CodeBLEU, with 2.75% higher compilation accuracy. Finally, CodeRosetta exhibits proficiency in Fortran to parallel C++ translation, marking it, to our knowledge, as the first encoder-decoder model for this complex task, improving CodeBLEU by at least 4.63 points compared to closed-source and open-code LLMs.
This paper addresses the challenge of accurately translating technical terms, which are crucial for clear communication in specialized fields. We introduce the Parenthetical Terminology Translation (PTT) task, designed to mitigate potential inaccuracies by displaying the original term in parentheses alongside its translation. To implement this approach, we generated a representative PTT dataset using a collaborative approach with large language models and applied knowledge distillation to fine-tune traditional Neural Machine Translation (NMT) models and small-sized Large Language Models (sLMs). Additionally, we developed a novel evaluation metric to assess both overall translation accuracy and the correct parenthetical presentation of terms. Our findings indicate that sLMs did not consistently outperform NMT models, with fine-tuning proving more effective than few-shot prompting, particularly in models with continued pre-training in the target language. These insights contribute to the advancement of more reliable terminology translation methodologies.
Building a reliable visual question answering~(VQA) system across different languages is a challenging problem, primarily due to the lack of abundant samples for training. To address this challenge, recent studies have employed machine translation systems for the cross-lingual VQA task. This involves translating the evaluation samples into a source language (usually English) and using monolingual models (i.e., translate-test). However, our analysis reveals that translated texts contain unique characteristics distinct from human-written ones, referred to as translation artifacts. We find that these artifacts can significantly affect the models, confirmed by extensive experiments across diverse models, languages, and translation processes. In light of this, we present a simple data augmentation strategy that can alleviate the adverse impacts of translation artifacts.
Given $λ\in\mathbb{R}$ and $\textbf{v}\in\mathbb{L}^3$, a $λ$-translator with velocity $\textbf{v}$ is an immersed surface in $\mathbb{L}^3$ whose mean curvature satisfies $H=\langle N,\textbf{v}\rangle+λ$, where $N$ is a unit normal vector field. When $λ=0$, we fall into the class of translating solitons of the mean curvature flow. In this paper we study $λ$-translators in $\mathbb{L}^3$ that are invariant under a 1-parameter group of translations and rotations. The former are cylindrical surfaces and explicit parametrizations are found, distinguishing on the causality of both the ruling direction and the $λ$-translators. In the case of rotational $λ$-translators we distinguish between spacelike and timelike rotations and exhibit the qualitative properties of rotational $λ$-translators by analyzing the non-linear autonomous system fulfilled by the coordinate functions of the generating curves.
By carrying out refined point-wise estimates for the mean curvature, we prove better rigidity theorems of Lagrangian and symplectic translating solitons.
Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with these divergences and translating pronouns is a challenge in machine translation. Mentions are referring candidates of pronouns and have closer relations with pronouns compared to general tokens. We assume that extracting additional mention features can help pronoun translation. Therefore, we introduce an additional mention attention module in the decoder to pay extra attention to source mentions but not non-mention tokens. Our mention attention module not only extracts features from source mentions, but also considers target-side context which benefits pronoun translation. In addition, we also introduce two mention classifiers to train models to recognize mentions, whose outputs guide the mention attention. We conduct experiments on the WMT17 English-German translation task, and evaluate our models on general translation and pronoun translation, using BLEU, APT, and contrastive evaluation metrics. Our proposed model outperforms the baseline Transformer model in terms of APT and BLEU scores, this confirms our hypothesis that we can improve pronoun translation by paying additional attention to source mentions, and shows that our introduced additional modules do not have negative effect on the general translation quality.
Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping functions to operate on entity embeddings. However, we find that such relation-level modeling cannot capture the diverse relational structures of KGs well. In this paper, we propose a novel edge-centric embedding model TransEdge, which contextualizes relation representations in terms of specific head-tail entity pairs. We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity embeddings. TransEdge achieves promising performance on different prediction tasks. Our experiments on benchmark datasets indicate that it obtains the state-of-the-art results on embedding-based entity alignment. We also show that TransEdge is complementary with conventional entity alignment methods. Moreover, it shows very competitive performance on link prediction.
This article is concerned with the significance of the editor in the translation process. The author presents the current status of editors in translation studies and calls for more attention to be paid to them. In the analytical part, the author analyses and presents selected examples of editorial changes of the first version of Yuri Slezkine’s House of Government translation into Polish. The analysis deals with passages where the editors corrected the translators’ mistakes. Particular attention is paid to fragments where the editors implemented difficult, potentially controversial corrections. This serves to show the role editors play in the translation process as well as the multitude and importance of the changes they introduce. The article stresses that the choices in the final translation that the audience, including translation scholars, reads are very often the editors’, not the translator’s, which should be taken into account while giving praise or assigning blame.
Questo articolo analizza la rappresentazione del lavoro considerando la sua profonda relazione con le questioni ambientali e il pensiero ecologico. Grazie a un'analisi ecopoetica di Strada Provinciale Tre di Vinci (2007), questa ricerca si propone di contribuire al dibattito critico-letterario intorno al lavoro mettendo a fuoco diversi aspetti solitamente non considerati: da un lato, l'interrelazione tra l'atto del lavorare e i cambiamenti ambientali, dall'altro, il potenziale potere delle immagini ecologiche legate al lavoro. Il primo aspetto sarà mostrato osservando gli effetti che la produzione capitalista ha sul corpo-lavoro e sul corpo-terra. Il secondo sarà esaminato attraverso tre diversi processi narrativi che contraddistinguono il romanzo di Vinci: la camminata immersiva, i ritmi naturali e antropici, gli aspetti sociali e spaziali delle pratiche mobili. Il quadro teorico combinerà la critica tematica del lavoro con gli studi sulle Mobilità e l'Ecopoetica.
Geography. Anthropology. Recreation, Language. Linguistic theory. Comparative grammar
Yu Ando, Nora Jee-Young Park and, Gun Oh Chong
et al.
Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.
We describe a translation from a fragment of SUMO (SUMO-K) into higher-order set theory. The translation provides a formal semantics for portions of SUMO which are beyond first-order and which have previously only had an informal interpretation. It also for the first time embeds a large common-sense ontology into a very secure interactive theorem proving system. We further extend our previous work in finding contradictions in SUMO from first order constructs to include a portion of SUMO's higher order constructs. Finally, using the translation, we can create problems that can be proven using higher-order interactive and automated theorem provers. This is tested in several systems and can be used to form a corpus of higher-order common-sense reasoning problems.