LEXICOGRAPHIC PROBLEMS OF MACHINE TRANSLATION SYSTEMS ON THE WAY FROM LITERAL TO NEURAL

The article discusses some current issues of interpreting out-of-vocabulary words by modern machine translation systems (MT systems) in the context of changing forms and ways of maintaining an automatic dictionary. It provides a critical outline of the typology of MT systems and strategies for their...

Full description

Saved in:
Bibliographic Details
Main Authors: Larisa N. Beliaeva, Olga N. Kamshilova
Format: Article
Language:English
Published: Volgograd State University 2024-11-01
Series:Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie
Subjects:
Online Access:https://l.jvolsu.com/index.php/en/archive-en/928-science-journal-of-volsu-linguistics-2024-vol-23-no-5/artificial-intelligence-potential-in-natural-language-processing-and-machine-translation/2836-beliaeva-l-n-kamshilova-o-n-lexicographic-problems-of-machine-translation-systems-on-the-way-from-literal-to-neural
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The article discusses some current issues of interpreting out-of-vocabulary words by modern machine translation systems (MT systems) in the context of changing forms and ways of maintaining an automatic dictionary. It provides a critical outline of the typology of MT systems and strategies for their development. It describes the impact of fast developing software and technologies on these strategies and analyzes the changes they bring into the forms of dictionary support. The research shows that the linguistic support and the structure of automatic dictionaries, whatever the MT system is, are fundamentally important for ensuring the quality of translation. Despite all the success of neural MT (NMT) systems, their automatically updated vocabulary databases do not record words characterized by terminological specificity and low frequency in the special texts and text corpora on which the system is trained. Analysis of translations performed by two popular NMT systems – Google Translate and Yandex Translate – has proven that they fail to process and unify the translation of words that are not entered in the system dictionaries, a task used to be solved easily by users of all types of MT systems with the help of automatic dictionaries. With statistic-based automatic dictionaries it remains a pressing problem and requires a special approach when editing MP results.
ISSN:1998-9911
2409-1979