  Multilingual Neural Machine Translation architectures mainly differ in the
amount of sharing modules and parameters among languages. In this paper, and
from an algorithmic perspective, we explore if the chosen architecture, when
trained with the same data, influences the gender bias accuracy. Experiments in
four language pairs show that Language-Specific encoders-decoders exhibit less
bias than the Shared encoder-decoder architecture. Further interpretability
analysis of source embeddings and the attention shows that, in the
Language-Specific case, the embeddings encode more gender information, and its
attention is more diverted. Both behaviors help in mitigating gender bias.
