  Antibodies are a critical part of the immune system, having the function of
directly neutralising or tagging undesirable objects (the antigens) for future
destruction. Being able to predict which amino acids belong to the paratope,
the region on the antibody which binds to the antigen, can facilitate antibody
design and contribute to the development of personalised medicine. The
suitability of deep neural networks has recently been confirmed for this task,
with Parapred outperforming all prior physical models. Our contribution is
twofold: first, we significantly outperform the computational efficiency of
Parapred by leveraging \`a trous convolutions and self-attention. Secondly, we
implement cross-modal attention by allowing the antibody residues to attend
over antigen residues. This leads to new state-of-the-art results on this task,
along with insightful interpretations.
