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Keywords = translationese

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14 pages, 490 KiB  
Article
Identifying Source-Language Dialects in Translation
by Sergiu Nisioi, Ana Sabina Uban and Liviu P. Dinu
Mathematics 2022, 10(9), 1431; https://doi.org/10.3390/math10091431 - 24 Apr 2022
Cited by 2 | Viewed by 3188
Abstract
In this paper, we aim to explore the degree to which translated texts preserve linguistic features of dialectal varieties. We release a dataset of augmented annotations to the Proceedings of the European Parliament that cover dialectal speaker information, and we analyze different classes [...] Read more.
In this paper, we aim to explore the degree to which translated texts preserve linguistic features of dialectal varieties. We release a dataset of augmented annotations to the Proceedings of the European Parliament that cover dialectal speaker information, and we analyze different classes of written English covering native varieties from the British Isles. Our analyses aim to discuss the discriminatory features between the different classes and to reveal words whose usage differs between varieties of the same language. We perform classification experiments and show that automatically distinguishing between the dialectal varieties is possible with high accuracy, even after translation, and propose a new explainability method based on embedding alignments in order to reveal specific differences between dialects at the level of the vocabulary. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications)
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14 pages, 344 KiB  
Article
Lexical Diversity in Statistical and Neural Machine Translation
by Mojca Brglez and Špela Vintar
Information 2022, 13(2), 93; https://doi.org/10.3390/info13020093 - 15 Feb 2022
Cited by 9 | Viewed by 5141
Abstract
Neural machine translation systems have revolutionized translation processes in terms of quantity and speed in recent years, and they have even been claimed to achieve human parity. However, the quality of their output has also raised serious doubts and concerns, such as loss [...] Read more.
Neural machine translation systems have revolutionized translation processes in terms of quantity and speed in recent years, and they have even been claimed to achieve human parity. However, the quality of their output has also raised serious doubts and concerns, such as loss in lexical variation, evidence of “machine translationese”, and its effect on post-editing, which results in “post-editese”. In this study, we analyze the outputs of three English to Slovenian machine translation systems in terms of lexical diversity in three different genres. Using both quantitative and qualitative methods, we analyze one statistical and two neural systems, and we compare them to a human reference translation. Our quantitative analyses based on lexical diversity metrics show diverging results; however, translation systems, particularly neural ones, mostly exhibit larger lexical diversity than their human counterparts. Nevertheless, a qualitative method shows that these quantitative results are not always a reliable tool to assess true lexical diversity and that a lot of lexical “creativity”, especially by neural translation systems, is often unreliable, inconsistent, and misguided. Full article
(This article belongs to the Special Issue Frontiers in Machine Translation)
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22 pages, 314 KiB  
Article
Post-Editese in Literary Translations
by Sheila Castilho and Natália Resende
Information 2022, 13(2), 66; https://doi.org/10.3390/info13020066 - 28 Jan 2022
Cited by 26 | Viewed by 5135
Abstract
In the present study, we investigated the post-editese phenomenon, i.e., the unique features that set machine translated post-edited texts apart from human-translated texts. We used two literary texts, namely, the English children’s novel by Lewis Carroll Alice’s Adventures in Wonderland (AW) and Paula [...] Read more.
In the present study, we investigated the post-editese phenomenon, i.e., the unique features that set machine translated post-edited texts apart from human-translated texts. We used two literary texts, namely, the English children’s novel by Lewis Carroll Alice’s Adventures in Wonderland (AW) and Paula Hawkins’ popular book The Girl on the Train (TGOTT). Both literary texts were Google translated from English into Brazilian Portuguese to investigate whether the post-editese features can be found on the surface of the post-edited (PE) texts. In addition, we examined how the features found in the PE texts differ from the features encountered in the human-translated (HT) and machine translation (MT) versions of the same source text. Results revealed evidence for post-editese for TGOTT only with PE versions being more similar to the MT output than to the HT texts. Full article
(This article belongs to the Special Issue Machine Translation for Conquering Language Barriers)
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