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1.
Reduction of Neural Machine Translation Failures by Incorporating Statistical Machine Translation
Jani Dugonik, Mirjam Sepesy Maučec, Domen Verber, Janez Brest, 2023, izvirni znanstveni članek

Opis: This paper proposes a hybrid machine translation (HMT) system that improves the quality of neural machine translation (NMT) by incorporating statistical machine translation (SMT). Therefore, two NMT systems and two SMT systems were built for the Slovenian-English language pair, each for translation in one direction. We used a multilingual language model to embed the source sentence and translations into the same vector space. From each vector, we extracted features based on the distances and similarities calculated between the source sentence and the NMT translation, and between the source sentence and the SMT translation. To select the best possible translation, we used several well-known classifiers to predict which translation system generated a better translation of the source sentence. The proposed method of combining SMT and NMT in the hybrid system is novel. Our framework is language-independent and can be applied to other languages supported by the multilingual language model. Our experiment involved empirical applications. We compared the performance of the classifiers, and the results demonstrate that our proposed HMT system achieved notable improvements in the BLEU score, with an increase of 1.5 points and 10.9 points for both translation directions, respectively.
Ključne besede: neural machine translation, statistical machine translation, sentence embedding, similarity, classification, hybrid machine translation
Objavljeno v DKUM: 20.02.2024; Ogledov: 322; Prenosov: 30
.pdf Celotno besedilo (400,40 KB)
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2.
Applicability and challenges of using machine translation in translator training
Melita Koletnik, 2011, strokovni članek

Opis: During the last decade, translation as well as translator training have experienced a significant change. This change has been significantly influenced by the development of the Internet and the successive availability of web-based translation resources, such as Google Translate. Their introduction into the translation didactic process and training is no longer a matter of a teacher’s personal preference and IT skills, but a necessity imposed by the ever-swifter advancement of technology. This article presents the experimental results of an ongoing broader research study focusing on the modes and frequency of use of the Internet, Google Translate and Google Translator Toolkit among translation students at the undergraduate level. The preliminary results, presented in this article, are based on a questionnaire which was prepared in relation to the use of Google Translate while considering the latest professional findings. The article concludes with the author’s observations as to the applicability of these resources in translator training and the challenges thereof.
Ključne besede: machine translation, teaching methodology, internet, Google Translate, machine translation systems, translator training, translation didactics, Internet, Google Translate
Objavljeno v DKUM: 12.05.2017; Ogledov: 2030; Prenosov: 237
.pdf Celotno besedilo (269,02 KB)
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3.
SUMAT : data collection and parallel corpus compilation for machine translation of subtitles
Volha Petukhova, Mirjam Sepesy Maučec, 2012, objavljeni znanstveni prispevek na konferenci

Opis: This paper describes the data collection and parallel corpus compilation activities carried out in the FP7 EU-funded SUMAT project. This project aims to develop an online subtitle translation service for nine European languages combined into 14 different language pairs. This data provides bilingual and monolingual training data for statistical machine translation engines which will semi-automate the subtitle translation processes of subtitling companies on a large scale.
Ključne besede: parallel multilingua corpora, statistical machine translation, subtitle translation service
Objavljeno v DKUM: 10.07.2015; Ogledov: 3057; Prenosov: 62
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