What does it take to create the Babel Fish, a tool that can help individuals translate speech
between any two languages? While recent breakthroughs in text-based models have …

Speech datasets are crucial for training Speech Language Technologies (SLT); however,
the lack of diversity of the underlying training data can lead to serious limitations in building …

Content Warning: This paper contains examples of misgendering and erasure that could be
offensive and potentially triggering. Gender bias in language technologies has been widely …

Abstract Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …

Gender inequality is embedded in our communication practices and perpetuated in
translation technologies. This becomes particularly apparent when translating into …

Natural language generation models reproduce and often amplify the biases present in their
training data. Previous research explored using sequence-to-sequence rewriting models to …

As generic machine translation (MT) quality has improved, the need for targeted
benchmarks that explore fine-grained aspects of quality has increased. In particular, gender …

Lexical disambiguation is a major challenge for machine translation systems, especially if
some senses of a word are trained less often than others. Identifying patterns of …

As part of the WMT-2023" Test suites" shared task, in this paper we summarize the results of
two test suites evaluations: MuST-SHE-WMT23 and INES. By focusing on the en-de and de …

Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a
pivotal challenge for the creation of more inclusive translation technologies. Advancements …