scholarly journals La constitución de una memoria terminológica: elaboración de terminologías bilingües en programas de memoria de traducción

2021 ◽  
Vol 3 ◽  
Author(s):  
Carles Tebé ◽  
María Teresa Cabré
Keyword(s):  
Target Sentence ◽  
Memory Unit ◽  
Translation Process ◽  
Translation Memory ◽  
Computer Aided ◽  
Source Sentence ◽  
Translation Systems

Computer-aided translation systems (CAT) based on Translation Memories (TM) are a widely diffused technology that uses database and code-protection features to improve the quality, efficiency and consistency of the human translation process. These systems basically consist of a textual database in which each source sentence of a translation is stored together with the target sentence (this is called a translation memory “unit”). New and changed translation proposals will then be stored in the database for future use. This textual database – the kernel of the system – is combined with a terminological database (TDB), which is used by translators to store independently, terminological equivalences or translation units of particular value.In this paper the authors outline a first draft of a methodology that describes the preparation of a bilingual terminology from – and within – TM applications. The bilingual corpus produced is called the ‘terminological memory’ of the translator.

scholarly journals Corpora and corpus technology for translation purposes in professional and academic environments. Major achievements and new perspectives

2016 ◽  
Vol 36 (1) ◽  
pp. 36 ◽  
Author(s):  
Cécile Frérot
Keyword(s):  
Empirical Studies ◽  
Working Environment ◽  
Added Value ◽  
Translation Process ◽  
Translation Memory ◽  
Academic Environments ◽  
Translation Quality ◽  
The Everyday ◽  
Computer Aided ◽  
Translator Training

http://dx.doi.org/10.5007/2175-7968.2016v36nesp1p36The “use” of corpora and concordancers in translation teaching has grown increasingly attractive since the mid1990s’ with an abundant literature advocating their use and promoting their benefits in the translation classroom. In translator training, efforts are being made to incorporate the use of corpora and concordancers in masters’ programmes and to offer specific modules on corpora for translation as the use of translation memory (TM) systems within Computer-Aided Translation (CAT) courses still dominates. In the translation profession, while TM systems are part of the everyday working environment, the same cannot be said of corpora and concordancers even though the most recent surveys show that professional translators would like to learn more about the potential of corpora for translation. Overall, the “usefulness” of corpora and corpus technology at the different stages of the translation process remains poorly documented in translation but a growing number of empirical studies has started to show concern as it has now become of paramount importance to assess the extent to which corpora are of added value for translation quality in both professional and academic environments.

scholarly journals Improving the Translation Environment for Professional Translators

Informatics ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 24 ◽  
Author(s):  
Vincent Vandeghinste ◽  
Tom Vanallemeersch ◽  
Liesbeth Augustinus ◽  
Bram Bulté ◽  
Frank Van Eynde ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Interface Design ◽  
Point Of View ◽  
Interaction Point ◽  
Translation Process ◽  
Terminology Extraction ◽  
Translation Quality ◽  
Computer Aided ◽  
Translation Systems ◽  
Computer Interaction

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.

scholarly journals Improving the Translation Environment for Professional Translators

2019 ◽  
Author(s):  
Vincent Vandeghinste ◽  
Tom Vanallemeersch ◽  
Liesbeth Augustinus ◽  
Bram Bulté ◽  
Frank Van Eynde ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Interface Design ◽  
Point Of View ◽  
Interaction Point ◽  
Translation Process ◽  
Terminology Extraction ◽  
Translation Quality ◽  
Computer Aided ◽  
Translation Systems ◽  
Computer Interaction

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.

Translation of Medical Texts using Neural Networks

2016 ◽  
Vol 5 (4) ◽  
pp. 51-66 ◽  
Author(s):  
Krzysztof Wolk ◽  
Krzysztof P. Marasek
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
European Medicines Agency ◽  
Translation System ◽  
Training Methods ◽  
Neural Machine Translation ◽  
Machine Translation System ◽  
Source Sentence ◽  
Parallel Text ◽  
Translation Systems

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.

A systems theory perspective on the translation process

2019 ◽  
Vol 2 (2) ◽  
pp. 211-232
Author(s):  
Michael Carl ◽  
Andrew Tonge ◽  
Isabel Lacruz
Keyword(s):  
Systems Theory ◽  
Dissipative Structures ◽  
Source Text ◽  
Translation Process ◽  
Gaze Patterns ◽  
Word Translation ◽  
Time And Energy ◽  
Translation Systems ◽  
Internal Order

Abstract The translation process has often been described as a sequence of three steps, source text (ST) analysis, source-target transfer, and target text (TT) generation. We propose a radically different view, in which the human translation process consists of a hierarchy of interacting word and phrase translations systems which organize and integrate as dissipative structures. Activation of word (or phrase) translation systems is a non-selective subliminal process in the translator’s mind not restricted to one language. Depending on the entropy (i.e., the internal order) of the word translation systems, a human translator spends more or less time and energy during the translation process, which can be measured in the form of gaze patterns and production duration.

scholarly journals The RNA chaperone protein CspA stimulates translation during cold acclimation by promoting the progression of the ribosomes

2021 ◽  
Author(s):  
Anna Maria Giuliodori ◽  
Riccardo Belardinelli ◽  
Melodie Duval ◽  
Raffaella Garofalo ◽  
Emma Schenckbecher ◽  
...  
Keyword(s):  
Low Temperature ◽  
Cold Acclimation ◽  
Rna Binding ◽  
Structural Rearrangement ◽  
Translation Process ◽  
Ribosomal Subunits ◽  
Mrna Structure ◽  
Elongation Phase ◽  
Translation Systems ◽  
Dependent Mechanism

CspA is an RNA binding protein expressed during cold-shock in Escherichia coli, capable of stimulating translation of several mRNAs - including its own - at low temperature. We used reconstituted translation systems to monitor the effects of CspA on the different steps of the translation process and probing experiments to analyze the interactions with its target mRNAs. We specifically focused on cspA mRNA which adopts a cold-induced secondary structure at temperatures below 20°C and a more closed conformation at 37°C. We show that at low temperature CspA specifically promotes the translation of the mRNA folded in the conformation less accessible to the ribosome (37°C form). CspA interacts with its mRNA without inducing large structural rearrangement, does not bind the ribosomal subunits and is not able to stimulate the formation of the translation initiation complexes. On the other hand, CspA promotes the progression of the ribosomes during translation of its mRNA at low temperature and this stimulation is mRNA structure-dependent. A similar structure-dependent mechanism may be responsible for the CspA-dependent translation stimulation observed with other probed mRNAs, for which the transition to the elongation phase is progressively facilitated during cold acclimation with the accumulation of CspA.

Source-side Reordering to Improve Machine Translation between Languages with Distinct Word Orders

2021 ◽  
Vol 20 (4) ◽  
pp. 1-18
Author(s):  
Karunesh Kumar Arora ◽  
Shyam Sunder Agrawal
Keyword(s):  
Machine Translation ◽  
Language Model ◽  
Target Language ◽  
Long Distance ◽  
Parts Of Speech ◽  
Source Sentence ◽  
The Subject ◽  
Statistical Systems ◽  
Translation Systems ◽  
The Impact

English and Hindi have significantly different word orders. English follows the subject-verb-object (SVO) order, while Hindi primarily follows the subject-object-verb (SOV) order. This difference poses challenges to modeling this pair of languages for translation. In phrase-based translation systems, word reordering is governed by the language model, the phrase table, and reordering models. Reordering in such systems is generally achieved during decoding by transposing words within a defined window. These systems can handle local reorderings, and while some phrase-level reorderings are carried out during the formation of phrases, they are weak in learning long-distance reorderings. To overcome this weakness, researchers have used reordering as a step in pre-processing to render the reordered source sentence closer to the target language in terms of word order. Such approaches focus on using parts-of-speech (POS) tag sequences and reordering the syntax tree by using grammatical rules, or through head finalization. This study shows that mere head finalization is not sufficient for the reordering of sentences in the English-Hindi language pair. It describes various grammatical constructs and presents a comparative evaluation of reorderings with the original and the head-finalized representations. The impact of the reordering on the quality of translation is measured through the BLEU score in phrase-based statistical systems and neural machine translation systems. A significant gain in BLEU score was noted for reorderings in different grammatical constructs.

Computer-aided translation as a distributed cognitive task

2006 ◽  
Vol 14 (2) ◽  
pp. 443-464 ◽  
Author(s):  
Barbara Dragsted
Keyword(s):  
Cognitive Processing ◽  
Cognitive Task ◽  
Sentence Structure ◽  
Source Text ◽  
Translation Process ◽  
Translation Memory ◽  
System A ◽  
Strong Focus ◽  
The Impact ◽  
Made In

The present article examines the potential effects on the translation process of working interactively with a translation memory (TM) system, a tool for storing and sharing previous translations. A TM system automatically divides the source text into sentences presented to the translator one-by-one. Based on observations made in an empirical study of six professional translators and six translation students, it is argued that full sentences do not constitute a central cognitive processing category in translation, and that the sentence-by-sentence presentation inherent in TM systems therefore creates an unnaturally strong focus on the sentence, which affects the very task of translation (as well as the translation product). Particular attention is given to the impact of the use of TM systems on the informants’ revision behaviour and their tendency to change the sentence structure.

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