Papers by Margot Fonteyne

2 papers
Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level (2020.lrec-1)

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Challenge: Several studies have demonstrated that translation quality has improved enormously since the emergence of neural machine translation systems.
Approach: They performed a document-level evaluation of the raw NMT output of an entire novel and annotated it in two steps: first all fluency errors, then all accuracy errors.
Outcome: The results show that translation quality has improved enormously since the emergence of neural machine translation systems.
GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation (2022.lrec-1)

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Challenge: Despite improvements in machine translation output, remarkable differences can be observed when comparing machine translations (MT) and human translations.
Approach: They describe a corpus of eye movement data collected during natural reading of a human translation and a machine translation of . they use this corpus to investigate the effect of machine translation on the reading process and the effects of various error types on reading.
Outcome: The proposed corpus will be used in future research to investigate the effect of machine translation on the reading process and the effects of various error types on reading.

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