Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics

Webster, Rebecca; Fonteyne, Margot; Tezcan, Arda; Macken, Lieve; Daems, Joke

doi:10.3390/informatics7030032

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…Webster et al [15] focused on examining the applicability of NMT in the field of literary translation. The authors studied the outputs of the Google Translate and DeepL MT systems.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of English–Slovak Neural and Statistical Machine Translation

et al. 2021

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This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak. As the statistical approach is the predecessor of neural machine translation, it was assumed that the neural network approach would generate results with a better quality. An experiment was performed using residuals to compare the scores of automatic metrics of the accuracy (BLEU_n) of the statistical machine translation with those of the neural machine translation. The results showed that the assumption of better neural machine translation quality regardless of the system used was confirmed. There were statistically significant differences between the SMT and NMT in favor of the NMT based on all BLEU_n scores. The neural machine translation achieved a better quality of translation of journalistic texts from English into Slovak, regardless of if it was a system trained on general texts, such as Google Translate, or specific ones, such as the European Commission’s (EC’s) tool, which was trained on a specific-domain.

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“…Webster et al [15] focused on examining the applicability of NMT in the field of literary translation. The authors studied the outputs of the Google Translate and DeepL MT systems.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of English–Slovak Neural and Statistical Machine Translation

et al. 2021

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“…Several corpora for genre classification have been developed over the years in multiple languages, such as English, Arabic, Spanish, and more [20][21][22][23]. Not much analogous research has been conducted on datasets in the Russian language.…”

Section: Related Workmentioning

confidence: 99%

Genre Classification of Books in Russian with Stylometric Features: A Case Study

Vanetik,

Tiamanova,

Kogan

et al. 2024

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Within the literary domain, genres function as fundamental organizing concepts that provide readers, publishers, and academics with a unified framework. Genres are discrete categories that are distinguished by common stylistic, thematic, and structural components. They facilitate the categorization process and improve our understanding of a wide range of literary expressions. In this paper, we introduce a new dataset for genre classification of Russian books, covering 11 literary genres. We also perform dataset evaluation for the tasks of binary and multi-class genre identification. Through extensive experimentation and analysis, we explore the effectiveness of different text representations, including stylometric features, in genre classification. Our findings clarify the challenges present in classifying Russian literature by genre, revealing insights into the performance of different models across various genres. Furthermore, we address several research questions regarding the difficulty of multi-class classification compared to binary classification, and the impact of stylometric features on classification accuracy.

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“…The excerpt-based approach enjoys advantage with formality, cohesion and contextual relevance. Active learning methods, on the contrary, do not have consecutive sentences and therefore lose local coherence and pose challenges to human translators (Muntés Mulero et al, 2012;Denkowski, 2015;Sperber et al, 2017;Maruf et al, 2019;Webster et al, 2020;Zhou and Waibel, 2021a;Salunkhe et al, 2016). This is an active research area.…”

Section: Future Workmentioning

confidence: 99%

Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages

Zhou,

Niehues,

Waibel

2023

Proceedings of the the Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

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In many humanitarian scenarios, translation into severely low resource languages often does not require a universal translation engine, but a dedicated text-specific translation engine. For example, healthcare records, hygienic procedures, government communication, emergency procedures and religious texts are all limited texts. While generic translation engines for all languages do not exist, translation of multilingually known limited texts into new, endangered languages may be possible and reduce human translation effort. We attempt to leverage translation resources from many rich resource languages to efficiently produce best possible translation quality for a well known text, which is available in multiple languages, in a new, severely low resource language. We examine two approaches: 1.) best selection of seed sentences to jump start translations in a new language in view of best generalization to the remainder of a larger targeted text(s), and 2.) we adapt large general multilingual translation engines from many other languages to focus on a specific text in a new, unknown language. We find that adapting large pretrained multilingual models to the domain/text first and then to the severely low resource language works best. If we also select a best set of seed sentences, we can improve average chrF performance on new test languages from a baseline of 21.9 to 50.7, while reducing the number of seed sentences to only ∼1,000 in the new, unknown language.

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Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics

Cited by 12 publications

References 23 publications

Evaluation of English–Slovak Neural and Statistical Machine Translation

Evaluation of English–Slovak Neural and Statistical Machine Translation

Genre Classification of Books in Russian with Stylometric Features: A Case Study

Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages

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