Search Results for author: Lieve Macken

Found 16 papers, 1 papers with code

Assessing the Comprehensibility of Automatic Translations (ArisToCAT)

no code implementations EAMT 2020 Lieve Macken, Margot Fonteyne, Arda Tezcan, Joke Daems

The ArisToCAT project aims to assess the comprehensibility of ‘raw’ (unedited) MT output for readers who can only rely on the MT output.

Literary translation as a three-stage process: machine translation, post-editing and revision

no code implementations EAMT 2022 Lieve Macken, Bram Vanroy, Luca Desmet, Arda Tezcan

This study focuses on English-Dutch literary translations that were created in a professional environment using an MT-enhanced workflow consisting of a three-stage process of automatic translation followed by post-editing and (mainly) monolingual revision.

Machine Translation Translation

Writing in a second Language with Machine translation (WiLMa)

no code implementations EAMT 2022 Margot Fonteyne, Maribel Montero Perez, Joke Daems, Lieve Macken

The WiLMa project aims to assess the effects of using machine translation (MT) tools on the writing processes of second language (L2) learners of varying proficiency.

Machine Translation Translation

GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation

no code implementations LREC 2022 Toon Colman, Margot Fonteyne, Joke Daems, Nicolas Dirix, Lieve Macken

In the present paper, we describe a large corpus of eye movement data, collected during natural reading of a human translation and a machine translation of a full novel.

Machine Translation Translation

LeConTra: A Learner Corpus of English-to-Dutch News Translation

2 code implementations LREC 2022 Bram Vanroy, Lieve Macken

Because we also collected translation process data in the form of keystroke logging, our dataset can be used as part of different research strands such as translation process research, learner corpus research, and corpus-based translation studies.

Sentence Sentence segmentation +1

Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level

no code implementations LREC 2020 Margot Fonteyne, Arda Tezcan, Lieve Macken

Several studies (covering many language pairs and translation tasks) have demonstrated that translation quality has improved enormously since the emergence of neural machine translation systems.

Machine Translation NMT +1

On the origin of errors: A fine-grained analysis of MT and PE errors and their relationship

no code implementations LREC 2014 Joke Daems, Lieve Macken, V, Sonia epitte

In order to improve the symbiosis between machine translation (MT) system and post-editor, it is not enough to know that the output of one system is better than the output of another system.

Machine Translation Translation

From keystrokes to annotated process data: Enriching the output of Inputlog with linguistic information

no code implementations LREC 2012 Lieve Macken, Veronique Hoste, Mari{\"e}lle Leijten, Luuk Van Waes

In this paper we report on an extension to the keystroke logging program Inputlog in which we aggregate the logged process data from the keystroke (character) level to the word level.

Speech Recognition

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