Automatic metrics for evaluating translation quality are typically validated by measuring how
well they correlate with human assessments. However, correlation methods tend to capture …

The question of context in neural machine translation often focuses on topics related to
document-level translation or intersentential context. However, there is a wide range of other …

Artificial intelligence (AI) systems have become an indispensable component of modern
technology. However, research on human behavioral responses is lagging behind, ie, the …

Effective communication is fundamental to any interaction, yet challenges arise when
participants do not share a common language. Automatic translation systems offer a …

Converging societal and technical factors have transformed language technologies into user-
facing applications employed across languages. Machine Translation (MT) has become a …

Recent advances in automatic quality estimation for machine translation have exclusively
focused on written language, leaving the speech modality underexplored. In this work, we …

The non-profit settlement sector in Canada supports newcomers in achieving successful
integration. This sector faces increasing operational pressures amidst rising immigration …

Most metrics evaluating Machine Translation (MT) claim their effectiveness by demonstrating
their ability to distinguish the quality of different MT systems over a large corpus (system …

As we introduce complex algorithmic systems into decision-making in high-stakes domains,
system designers need principled approaches to help people set their expectations of these …