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Found 12 papers, 1 papers with code
Literary translation as a three-stage process: machine translation, post-editing and revision
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.
Dynamic Adaptation of Neural Machine-Translation Systems Through Translation Exemplars
This project aims to study the impact of adapting neural machine translation (NMT) systems through translation exemplars, determine the optimal similarity metric(s) for retrieving informative exemplars, and, verify the usefulness of this approach for domain adaptation of NMT systems.
Assessing the Comprehensibility of Automatic Translations (ArisToCAT)
The ArisToCAT project aims to assess the comprehensibility of ‘raw’ (unedited) MT output for readers who can only rely on the MT output.
Towards a Better Integration of Fuzzy Matches in Neural Machine Translation through Data Augmentation
We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT).
Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level
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.
Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation
We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM).
