This article examines common translation errors that occur in the translation of legal texts. In particular, it focuses on how German texts containing legal terminology are rendered into Modern Greek ...by the Google translation machine. Our case study is the Google-assisted translation of the original (German) version of the Constitution of the Federal Republic of Germany into Modern Greek. A training method is proposed for phrase extraction based on the occurrence frequency, which goes through the Skip-gram algorithm to be then integrated into the Self Attention Mechanism proposed by Vaswani et al. (2017) in order to minimise human effort and contribute to the development of a robust machine translation system for multi-word legal terms and special phrases. This Neural Machine Translation approach aims at developing vectorised phrases from large corpora and process them for translation. The research direction is to increase the in-domain training data set and enrich the vector dimension with more information for legal concepts (domain specific features).
This article examines common translation errors that occur in the translation of legal texts. In particular, it focuses on how German texts containing legal terminology are rendered into Modern Greek ...by the Google translation machine. Our case study is the Google-assisted translation of the original (German) version of the Constitution of the Federal Republic of Germany into Modern Greek. A training method is proposed for phrase extraction based on the occurrence frequency, which goes through the Skip-gram algorithm to be then integrated into the Self Attention Mechanism proposed by Vaswani et al. (2017) in order to minimise human effort and contribute to the development of a robust machine translation system for multi-word legal terms and special phrases. This Neural Machine Translation approach aims at developing vectorised phrases from large corpora and process them for translation. The research direction is to increase the in-domain training data set and enrich the vector dimension with more information for legal concepts (domain specific features).
This article examines common translation errors that occur in the translation of legal texts. In particular, it focuses on how German texts containing legal terminology are rendered into Modern Greek ...by the Google translation machine. Our case study is the Google-assisted translation of the original (German) version of the Constitution of the Federal Republic of Germany into Modern Greek. A training method is proposed for phrase extraction based on the occurrence frequency, which goes through the Skip-gram algorithm to be then integrated into the Self Attention Mechanism proposed by Vaswani et al. (2017) in order to minimise human effort and contribute to the development of a robust machine translation system for multi-word legal terms and special phrases. This Neural Machine Translation approach aims at developing vectorised phrases from large corpora and process them for translation. The research direction is to increase the in-domain training data set and enrich the vector dimension with more information for legal concepts (domain specific features).
The present application translates German financial terms to the Greek language aiming to maximize the translation quality. The demand for better translation increases the need for more MT systems; ...journalists or professionals in the banking sector require accurate translations. A novel statistical method of MT is presented, where we use a probability distribution over sentence pairs from a parallel corpus (German-Greek). Both the MT system and the parallel corpus were built from scratch. The present application combines parameters in a new way and estimates the probabilities that extract the best translation.
Context:
Holoprosencephaly (HPE) is a developmental defect characterized by wide phenotypic variability, ranging from minor midline malformations (eg, single central incisor) to severe deformities. ...In 10–15% of HPE patients, mutations in specific genes have been identified (eg, SHH, TGIF, SIX3). Pituitary stalk interruption syndrome (PSIS) constitutes a distinct abnormality of unknown pathogenesis, whereas isolated pituitary hypoplasia (IPH) has been linked to various developmental genes.
Objective:
Three of our patients with PSIS had a single central incisor, a malformation encountered in some HPE cases. Based on this observation, we initiated a search for mutations in HPE-associated genes in 30 patients with PSIS or IPH.
Design and Participants:
The entire coding region of the TGIF, SHH, and SIX3 genes was sequenced in patients with combined pituitary hormone deficiency associated with either PSIS or IPH and in healthy controls.
Results:
Two novel mutations in the HPE-related genes were detected (ie, c.799 C>T, p.Q267X in the TGIF gene, and c.1279G>A, p.G427R in the SHH gene) in 2 of our patients. The overall incidence of HPE-related gene mutations in our nonsyndromic and nonchromosomal patients was 6.6%. No molecular defect in the SIX3 gene was detected in our cohort.
Conclusions:
The data suggest that HPE-related gene mutations are implicated in the etiology of isolated pituitary defects (PSIS or IPH). Alternatively, PSIS or IPH may constitute mild forms of an expanded HPE spectrum.
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