|Publication type:||Conference other|
|Type of review:||Peer review (abstract)|
|Title:||I looked it up in DeepL : online dictionaries and online machine translation|
|Authors:||Delorme Benites, Alice|
Cotelli Kureth, Sara
|Conference details:||Tralogy3 : Tralogy: Human translation and natural language processing: Forging a new consensus?, Paris, France, 7-8 April 2022|
|Subjects:||Machine translation literacy; Language learning|
|Subject (DDC):||410.285: Computational linguistics |
418.02: Translating and interpreting
|Abstract:||The advent of neural machine translation in 2016 catapulted machine translation (MT) into a world that was already digitalizing at an accelerated pace. The last decade has also witnessed the digitalization of most well-established dictionaries (Merriam-Webster, Robert, Pons, Duden, etc.), including free online access and the emergence of bilingual concordancers (Linguee). As a result, language learners and translation students can use numerous online resources with a simple click. However, the process behind many online dictionaries (OD) is still human lexicographic work, while MT provides output based on natural language processing, i.e. artificial intelligence (AI), without human validation. A survey carried out among Swiss universities in 2021 shows that, in the context of higher education, machine translation tends to be used as a surrogate for online dictionaries. This chimes with observations by Briggs (2018) and O'Neill (2019), who noted an almost identical use of MT and OD amongst students. In the present survey, a large proportion of the respondents indicate that they use online MT systems primarily to look up words or to find synonyms. This conflation of MT and OD works both ways: respondents cited bilingual dictionaries when asked what MT systems they use. Further, the physical distinction between the two types of linguistic resources is also fading, since many online MT tools now include dictionary functions and vice versa. In this situation, the authority of human lexicographers seems to be tacitly challenged by AI and demonstrates the need to elucidate how human knowledge and AI should interact for building, maintaining and using linguistic resources. This communication will present the data consolidating this affirmation and will discuss practical consequences and implications, especially with respect to training needs in the area of MT literacy (Bowker 2020) and the informed use of digital literacy tools.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Organisational Unit:||Institute of Translation and Interpreting (IUED)|
|Published as part of the ZHAW project:||Digital Literacy im Hochschulkontext|
|Appears in collections:||Publikationen Angewandte Linguistik|
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Delorme Benites, A., Cotelli Kureth, S., Lehr, C., Steele, E., & Franciello, L. (2022, April 8). I looked it up in DeepL : online dictionaries and online machine translation. Tralogy3 : Tralogy: Human Translation and Natural Language Processing: Forging a New Consensus?, Paris, France, 7-8 April 2022.
Delorme Benites, A. et al. (2022) ‘I looked it up in DeepL : online dictionaries and online machine translation’, in Tralogy3 : Tralogy: Human translation and natural language processing: Forging a new consensus?, Paris, France, 7-8 April 2022.
A. Delorme Benites, S. Cotelli Kureth, C. Lehr, E. Steele, and L. Franciello, “I looked it up in DeepL : online dictionaries and online machine translation,” in Tralogy3 : Tralogy: Human translation and natural language processing: Forging a new consensus?, Paris, France, 7-8 April 2022, Apr. 2022.
Delorme Benites, Alice, et al. “I Looked It up in DeepL : Online Dictionaries and Online Machine Translation.” Tralogy3 : Tralogy: Human Translation and Natural Language Processing: Forging a New Consensus?, Paris, France, 7-8 April 2022, 2022.
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