Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI)
algorithms present promising solutions to a variety of technological and non-technological …

Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

Language can be used as a means of reproducing and enforcing harmful stereotypes and
biases and has been analysed as such in numerous research. In this paper, we present a …

Measuring bias is key for better understanding and addressing unfairness in NLP/ML
models. This is often done via fairness metrics, which quantify the differences in a model's …

Large-scale automatic speech translation systems today lack key features that help machine-
mediated communication feel seamless when compared to human-to-human dialogue. In …

The rise of concern around Natural Language Processing (NLP) technologies containing
and perpetuating social biases has led to a rich and rapidly growing area of research …

Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT).
The community proposed specific network architectures and learning-based methods to …

All language is characterised by variation which language users employ to construct
complex social identities and express social meaning. Like other machine learning …

The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

Abstract Machine translation (namely MT) has been one of the most popular fields in
computational linguistics and Artificial Intelligence (AI). As one of the most promising …