166 citations · 238 across the 14 of their papers we have counts for
15 papers
ConGA: Guidelines for Contextual Gender Annotation. A Framework for Annotating Gender in Machine Translation
Argentina Anna Rescigno, Eva Vanmassenhove, Johanna Monti
Handling gender across languages remains a persistent challenge for Machine Translation (MT) and Large Language Models (LLMs), especially when translating from gender-neutral langu…
Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures
Chiara Manna, Hosein Mohebbi, Afra Alishahi +2
While Large Language Models achieve state-of-the-art results across a wide range of NLP tasks, they remain prone to systematic biases. Among these, gender bias is particularly sali…
Losing our Tail, Again: (Un)Natural Selection & Multilingual LLMs
Eva Vanmassenhove
Multilingual Large Language Models considerably changed how technologies influence language. While previous technologies could mediate or assist humans, there is now a tendency to…
AI in Support of Diversity and Inclusion
Çiçek Güven, Afra Alishahi, Henry Brighton +7
In this paper, we elaborate on how AI can support diversity and inclusion and exemplify research projects conducted in that direction. We start by looking at the challenges and pro…
Gender Bias in Machine Translation and The Era of Large Language Models
Eva Vanmassenhove
This chapter examines the role of Machine Translation in perpetuating gender bias, highlighting the challenges posed by cross-linguistic settings and statistical dependencies. A co…
Tailoring Domain Adaptation for Machine Translation Quality Estimation
Javad Pourmostafa Roshan Sharami, Dimitar Shterionov, Frédéric Blain +4
While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular…
The Ecological Footprint of Neural Machine Translation Systems
Dimitar Shterionov, Eva Vanmassenhove
Over the past decade, deep learning (DL) has led to significant advancements in various fields of artificial intelligence, including machine translation (MT). These advancements wo…
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…
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation
Eva Vanmassenhove, Dimitar Shterionov, Andy Way
This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show ho…
ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction
Eva Vanmassenhove, Amit Moryossef, Alberto Poncelas +2
We present our system for the CLIN29 shared task on cross-genre gender detection for Dutch. We experimented with a multitude of neural models (CNN, RNN, LSTM, etc.), more "traditio…