Papers by Lieve Macken
LeConTra: A Learner Corpus of English-to-Dutch News Translation (2022.lrec-1)
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| Challenge: | a dataset of English-to-Dutch news translations enriched with translation process data is available for free . three students of a Master's programme in Translation were asked to translate 50 different English journalistic texts of approximately 250 tokens each. |
| Approach: | They propose to make a learner corpus of English-to-Dutch news translations enriched with translation process data. |
| Outcome: | The dataset can be used in translation process research, learner corpus research, and corpus-based translation studies. |
LiTransProQA: An LLM-based Literary Translation Evaluation Metric with Professional Question Answering (2025.emnlp-main)
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| Challenge: | Existing evaluation metrics for literature prioritize mechanical accuracy over artistic expression . this bias could result in an irreversible decline in translation quality and cultural authenticity . |
| Approach: | They propose a novel, reference-free, LLM-based question-answering framework for literary translation evaluation. |
| Outcome: | a novel, reference-free, LLM-based question-answering framework is developed for literary translation evaluation. |
Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used for creative tasks such as literary translation. |
| Approach: | They propose a paired-task framework that assesses translational creativity using Units of Creative Potential (UCPs) they benchmark 23 models and four creativity-oriented prompts to assess translational comprehension . |
| Outcome: | The proposed framework compares 23 models and four creativity-oriented prompts on literary excerpts from 11 books. |
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. |
A fine-grained error analysis of NMT, SMT and RBMT output for English-to-Dutch (L18-1)
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| Challenge: | Since 2016, the landscape of automated translation has substantially changed with the arrival of neural machine translation (NMT). |
| Approach: | They propose to use an annotated SCATE corpus of MT errors to enrich the SCATE error taxonomy to fit the neural MT output. |
| Outcome: | The proposed system outperforms phrase-based and rule-based systems except for lexical issues. |