TCR-epitope binding is the key mechanism for T cell regulation. Computational prediction of whether a given pair binds is of great interest for understanding the underlying science as well as various clinical applications. Previously developed methods do not account for interrelationship between amino acids and suffer from poor out-of-sample performance. Our model uses the multi-head self attention mechanism to capture biological contextual information and to improve generalization performance. We show that ours outperforms other models and we also demonstrate that the use of attention matrices can improve out-of-sample performance on recent SARS-CoV-2 data.