This study examines the effectiveness of
adaptive machine translation (AMT) for
gender-neutral language (GNL) use in
English-German translation using the
ModernMT engine. It investigates gender bias
in initial output and adaptability to two distinct
GNL strategies, as well as the influence of
translation memory (TM) use on adaptivity.
Findings indicate that despite inherent gender
bias, machine translation (MT) systems show
potential for adapting to GNL with appropriate
exposure and training, highlighting the
importance of customisation, exposure to
diverse examples, and better representation of
different forms for enhancing gender-fair
translation strategies.