105 citations · 118 across the 13 of their papers we have counts for
22 papers · 1 filter
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?
Kwabena Amponsah-Kaakyire, Daria Pylypenko, Josef van Genabith +1
Recent work has shown that neural feature- and representation-learning, e.g. BERT, achieves superior performance over traditional manual feature engineering based approaches, with…
Exploiting Social Media Content for Self-Supervised Style Transfer
Dana Ruiter, Thomas Kleinbauer, Cristina España-Bonet +2
Recent research on style transfer takes inspiration from unsupervised neural machine translation (UNMT), learning from large amounts of non-parallel data by exploiting cycle consis…
Towards Debiasing Translation Artifacts
Koel Dutta Chowdhury, Rricha Jalota, Cristina España-Bonet +1
Cross-lingual natural language processing relies on translation, either by humans or machines, at different levels, from translating training data to translating test sets. However…
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification
Daria Pylypenko, Kwabena Amponsah-Kaakyire, Koel Dutta Chowdhury +2
Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts. By contrast, to date, neural…
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages
Dana Ruiter, Dietrich Klakow, Josef van Genabith +1
For most language combinations, parallel data is either scarce or simply unavailable. To address this, unsupervised machine translation (UMT) exploits large amounts of monolingual…
Linguistically inspired morphological inflection with a sequence to sequence model
Eleni Metheniti, Guenter Neumann, Josef van Genabith
Inflection is an essential part of every human language's morphology, yet little effort has been made to unify linguistic theory and computational methods in recent years. Methods…