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The Impact of Alcohol on L1 versus L2
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In: ISSN: 0023-8309 ; Language and Speech ; https://hal.archives-ouvertes.fr/hal-03476236 ; Language and Speech, SAGE Publications (UK and US), 2021, 64 (3), pp.681-692. ⟨10.1177/0023830920953169⟩ (2021)
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A review of data collection practices using electromagnetic articulography
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In: ISSN: 1868-6354 ; Laboratory Phonology : Journal of the Association for Laboratory Phonology ; https://hal.archives-ouvertes.fr/hal-03476230 ; Laboratory Phonology : Journal of the Association for Laboratory Phonology, De Gruyter, 2021, 12 (1), pp.6. ⟨10.5334/labphon.237⟩ (2021)
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A review of data collection practices using electromagnetic articulography
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 12, No 1 (2021); 6 ; 1868-6354 (2021)
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Estimating the Level and Direction of Phonetic Dialect Change in the Northern Netherlands ...
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NDI WAVE and NDI VOX accuracy assessment (Rebernik et al., 2021) ...
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NDI WAVE and NDI VOX accuracy assessment (Rebernik et al., 2021) ...
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Adapting Monolingual Models: Data can be Scarce when Language Similarity is High ...
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Adapting Monolingual Models: Data can be Scarce when Language Similarity is High ...
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LSDC - A Comprehensive Dataset for Low Saxon Dialect Classification ...
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Neural Representations for Modeling Variation in Speech ...
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| 13 |
code – Supplemental material for The Impact of Alcohol on L1 versus L2 ...
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| 14 |
code – Supplemental material for The Impact of Alcohol on L1 versus L2 ...
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| 15 |
The Impact of Alcohol on L1 versus L2
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In: Lang Speech (2020)
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| 16 |
A New Acoustic-Based Pronunciation Distance Measure
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In: Front Artif Intell (2020)
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| 17 |
Back From the Future: Nonlinear Anticipation in Adults' and Children's Speech
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In: ISSN: 1092-4388 ; EISSN: 1558-9102 ; Journal of Speech, Language, and Hearing Research ; https://hal.archives-ouvertes.fr/hal-03476240 ; Journal of Speech, Language, and Hearing Research, American Speech-Language-Hearing Association, 2019, 62 (8S), pp.3033-3054. ⟨10.1044/2019_jslhr-s-csmc7-18-0208⟩ (2019)
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| 18 |
Post-editing effort of a novel with statistical and neural machine translation
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In: Toral, Antonio, Wieling, Martijn and Way, Andy orcid:0000-0001-5736-5930 (2018) Post-editing effort of a novel with statistical and neural machine translation. Frontiers in Digital Humanities, 5 (9). pp. 1-11. ISSN 2297-2668 (2018)
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| 19 |
Post-editing effort of a novel with statistical and neural machine translation
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In: Toral, Antonio orcid:0000-0003-2357-2960 , Wieling, Martijn orcid:0000-0003-0434-1526 and Way, Andy orcid:0000-0001-5736-5930 (2018) Post-editing effort of a novel with statistical and neural machine translation. Frontiers in Digital Humanities, 5 . ISSN 2297-2668 (2018)
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Abstract:
We conduct the first experiment in the literature in which a novel is translated automatically and then post-edited by professional literary translators. Our case study is Warbreaker, a popular fantasy novel originally written in English, which we translate into Catalan. We translated one chapter of the novel (over 3,700 words, 330 sentences) with two data-driven approaches to Machine Translation (MT): phrase-based statistical MT (PBMT) and neural MT (NMT). Both systems are tailored to novels; they are trained on over 100 million words of fiction. In the post-editing experiment, six professional translators with previous experience in literary translation translate subsets of this chapter under three alternating conditions: from scratch (the norm in the novel translation industry), post-editing PBMT, and post-editing NMT. We record all the keystrokes, the time taken to translate each sentence, as well as the number of pauses and their duration. Based on these measurements, and using mixed-effects models, we study post-editing effort across its three commonly studied dimensions: temporal, technical and cognitive. We observe that both MT approaches result in increases in translation productivity: PBMT by 18%, and NMT by 36%. Post-editing also leads to reductions in the number of keystrokes: by 9% with PBMT, and by 23% with NMT. Finally, regarding cognitive effort, post-editing results in fewer (29 and 42% less with PBMT and NMT, respectively) but longer pauses (14 and 25%).
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Keyword:
Machine learning; Machine translating
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URL: http://doras.dcu.ie/24601/
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Data for: Generalized additive modeling to analyze dynamic phonetic data: a tutorial focusing on articulatory differences between L1 and L2 speakers of English ...
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