49 citations · 57 across the 5 of their papers we have counts for
8 papers
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives
Eva Vanmassenhove, Chris Emmery, Dimitar Shterionov
Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive…
GENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena
Eva Vanmassenhove, Johanna Monti
Languages differ in terms of the absence or presence of gender features, the number of gender classes and whether and where gender features are explicitly marked. These cross-lingu…
Generating Gender Augmented Data for NLP
Nishtha Jain, Maja Popovic, Declan Groves +1
Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and…
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation
Eva Vanmassenhove, Dimitar Shterionov, Matthew Gwilliam
Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data. The ampl…
On the Integration of LinguisticFeatures into Statistical and Neural Machine Translation
Eva Vanmassenhove
New machine translations (MT) technologies are emerging rapidly and with them, bold claims of achieving human parity such as: (i) the results produced approach "accuracy achieved b…
Getting Gender Right in Neural Machine Translation
Eva Vanmassenhove, Christian Hardmeier, Andy Way
Speakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such d…