Assessment of the Translation and Post-Editing of Machine Translation (MT) With Special Reference to Chinese-English Translation

The current research reports the real performance of machine translation engines (DeepL and GPT-3.5) in translating Classical Chinese into Modern English as well as the post-editing quality of GPT-3.5. The statistical data reveals that: 1) machine translation saves more time and processing energy than human translators; 2) GPT-3.5’s performance in Chinese-English translation is better than Deepl, and it has the advantage of post-editing and self-evolution; 3) Human translators’ ability of semantic processing is superior than DeepL and GPT-3.5. Thus human translators and machine translation engines shall have a good cooperation in improving the accuracy, comprehensibility and fluency of translated texts

Machine Translation and Neural Networks for a multilingual EU

This paper presents an overview of the current developments and use of Machine Translation (MT) and Neural Machine Translation (NMT), specifically eTranslation, in the European Institutions. An insight into the state-of-the-art of NMT as currently in development in the Directorate-General for Translation (DG TRAD) of the European Parliament is provided by Pascale Chartier-Brun. Problems in machine translation support requiring further research and development for processing languages with complex morphosyntax are discussed in the outlook. This paper was developed from the presentation “IT integrated environment for optimising the translation of legislative documents in the EP“ by Pascale Chartier-Brun at the workshop “Europäische Rechtslinguistik und Digitale Möglichkeiten / EU Legal Linguistics and Digital Perspectives“, held at the University of Cologne July 7th/8th, 2017

THE TRANSLATION OF ENTERTAINMENT NEWS FROM ENGLISH TO INDONESIAN WITH MACHINE TRANSLATION

News has been spread internationally since it was digitalized. This situation makes machine translation used as a tool to solve the language barrier problem, as it is cheap and fast compared to human translators. However, the translation by Machine Translation is not always correct. In fact, it results in more problems than in successful translation; in other words, the use of this machine is like ‘garbage in, garbage out’. However, not many studies have been conducted to provide evidence of the weaknesses of machine translation. This research paper attempts to discover the translation methods and procedures of the “translate to Indonesian” featured by Google Translate in the translation of CNN International current news from English to Indonesian. The data consist of 10 pieces of entertainment news that are published online on CNN International News. A descriptive-qualitative approach is used to analyze the data. The scope of the analysis is lexical words only. The translated news was observed and compared to the original news in order to identify the methods and procedures applied in the translation results by the Machine Translation. The results of this analysis reveal that the “translate to Indonesian” feature from Google Translate commonly uses the literal and faithful translation methods and the procedure mostly found is borrowing. Consequently, the translation by this machine is still awkward and requires substantial improvement

Prerequisites For Shallow-Transfer Machine Translation Of Mordvin Languages : Language Documentation With A Purpose

This paper presents the current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research. We seek to illustrate and outline initial problem types to be encountered in the construction of an Apertium-based shallow-transfer machine translation system for the Mordvin language forms. We indicate reference points within Mordvin Studies and other parts of Uralic studies, as a point of departure for outlining a linguistic studies with a means for measuring its own progress and developing a roadmap for further studies. Keywords: Erzya, Moksha, Uralic, Shallow-transfer machine translation, Measurable language research, Measurable language distance, Finite-State Morphology, Universal DependenciesPeer reviewe

Towards the Creation of a Poetry Translation Mapping System

The translation of poetry is a complex, multifaceted challenge: the translated text should communicate the same meaning, similar metaphoric expressions, and also match the style and prosody of the original poem. Research on machine poetry translation is existing since 2010, but for four reasons it is still rather insufficient: 1. The few approaches existing completely lack any knowledge about current developments in both lyric theory and translation theory. 2. They are based on very small datasets. 3. They mostly ignored the neural learning approach that superseded the long-standing dominance of phrase-based approaches within machine translation. 4. They have no concept concerning the pragmatic function of their research and the resulting tools. Our paper describes how to improve the existing research and technology for poetry translations in exactly these four points. With regards to 1) we will describe the “Poetics of Translation”. With regards to 2) we will introduce the Worlds largest corpus for poetry translations from lyrikline. With regards to 3) we will describe first steps towards a neural machine translation of poetry. With regards to 4) we will describe first steps towards the development of a poetry translation mapping system