  Transformer-based encoder-decoder models produce a fused token-wise
representation after every encoder layer. We investigate the effects of
allowing the encoder to preserve and explore alternative hypotheses, combined
at the end of the encoding process. To that end, we design and examine a
$\textit{Multi-stream Transformer}$ architecture and find that splitting the
Transformer encoder into multiple encoder streams and allowing the model to
merge multiple representational hypotheses improves performance, with further
improvement obtained by adding a skip connection between the first and the
final encoder layer.
