scholarly journals Cadres, scripts, registres — complexité de traduction des mots polysémiques

2020 ◽  
Vol 32 ◽  
pp. 348-368
Author(s):  
Beata Śmigielska

The article deals with issues related to the description of polysemic units for supervised automatic translation of texts from French to Polish based on the object-oriented method by W. Banyś (AOO). Applying to the description additional tools allowing for the disambiguation — frames, scripts and register analysis — the author focuses on presenting the complexity of the description of lexical units. It should be stressed that the decision to assign meanings of such words one or another equivalents in the target language is determined by the criterion of preference. Some of the meanings of the word are easy to describe in such a way that the program can make a correct translation into the target language with a high degree of probability. However, there are often such meanings whose description is much more complex. The closer the meanings of the disambiguated words in the original and target language and the more similar the communication situation in which they are used, the more difficult it is to clearly indicate the boundaries between the meanings. In such cases, there is a much greater range of preferential character of the translation, which we have to adopt The French noun conjunction (f), chosen for analysis, reflects this phenomenon very well.

2021 ◽  
pp. 238-256
Author(s):  
Amal Arrame

Translation is not simple transpositions operations or transcoding processes from one language to another, it involves complex mental processes where linguistics alone cannot be sufficient. It is a communication situation between two languages, Arabic and French in this case, where the objective of the translator is the transmission of his final product in a clear way, respecting the meaning and the author intention of the original version. Translation of phrases is a real dilemma for translators; however, it turns out that it is a necessity in order to discover the other, and to try to keep the same effect as the source text by giving it a stylistic touch typical to the target language. To this end, we have carefully chosen the corpus that we have translated. A corpus that reflects the originality of the Arabic language and the possibility of reducing the linguistic, cultural and discursive gaps between Arabic and French through translation. The translation processes we have chosen, take into account the target language, French in this case, its idioms, phrases and proverbs inventory, its particularity and, finally, its ability to comprehend the idea contained in the idioms of the source language.


2013 ◽  
Vol 694-697 ◽  
pp. 2291-2294
Author(s):  
Xiao Bo Yang ◽  
Bang Ze Chen

By using object oriented method design graph vertices into class, and in this foundation to increase visual member, realize from the source point to the other vertex of the shortest path algorithm of dynamic visualization. Around the two sync window animation, the left window with thick lines drawn through the vertices and edges, " revealed the source point to the other vertex of the shortest path " list box display shortest path sequence and path length, the right window demonstration algorithm dynamic implementation process, and in the " S " text box to display the current most find shortest path vertices. The system has friendly interface, visual image.


2019 ◽  
Vol 28 (3) ◽  
pp. 447-453 ◽  
Author(s):  
Sainik Kumar Mahata ◽  
Dipankar Das ◽  
Sivaji Bandyopadhyay

Abstract Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.


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