Search Results for author:
Found 15 papers, 3 papers with code
“Vaderland”, “Volk” and “Natie”: Semantic Change Related to Nationalism in Dutch Literature Between 1700 and 1880 Captured with Dynamic Bernoulli Word Embeddings
From the analysis, it appears that “natie”, “volk” and “vaderlan”’ became more nationalistically-loaded over time.
A Comparison of Different NMT Approaches to Low-Resource Dutch-Albanian Machine Translation
Even when the available parallel data (NL↔SQ) was added, i. e. in a few-shot setting (FST), it remained the worst performing system according to the automatic (BLEU and TER) and human evaluation.
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives
Recent years have seen an increasing need for gender-neutral and inclusive language.
Gender Bias in Machine Translation and The Era of Large Language Models
This chapter examines the role of Machine Translation in perpetuating gender bias, highlighting the challenges posed by cross-linguistic settings and statistical dependencies.
Tailoring Domain Adaptation for Machine Translation Quality Estimation
While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data.
The Ecological Footprint of Neural Machine Translation Systems
This chapter focuses on the ecological footprint of neural MT systems.
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender-Neutral Alternatives
Recent years have seen an increasing need for gender-neutral and inclusive language.
GENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena
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.
Generating Gender Augmented Data for NLP
The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data.
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation
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.
On the Integration of LinguisticFeatures into Statistical and Neural Machine Translation
Establishing the discrepancies between the strengths of statistical approaches to MT and the way humans translate has been the starting point of our research.
Getting Gender Right in Neural Machine Translation
Our contribution is two-fold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs.
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation
This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT).
ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction
In contradiction with the results described in previous comparable shared tasks, our neural models performed better than our best traditional approaches with our best feature set-up.
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags
In this paper we incorporate semantic supersensetags and syntactic supertag features into EN{--}FR and EN{--}DE factored NMT systems.
