  This work presents an analysis of the effectiveness of using standard shallow
feed-forward networks to mimic the behavior of the attention mechanism in the
original Transformer model, a state-of-the-art architecture for
sequence-to-sequence tasks. We substitute key elements of the attention
mechanism in the Transformer with simple feed-forward networks, trained using
the original components via knowledge distillation. Our experiments, conducted
on the IWSLT2017 dataset, reveal the capacity of these "attentionless
Transformers" to rival the performance of the original architecture. Through
rigorous ablation studies, and experimenting with various replacement network
types and sizes, we offer insights that support the viability of our approach.
This not only sheds light on the adaptability of shallow feed-forward networks
in emulating attention mechanisms but also underscores their potential to
streamline complex architectures for sequence-to-sequence tasks.
