Abstract:<p>Attention networks often make decisions relying solely on a few pieces of tokens, even if those reliances are not truly indicative of the underlying meaning or intention of the full context. That can lead to over-fitting in Transformers and hinder their ability to generalize. Attention regularization and sparsity-based methods have been used to overcome this issue. However, these methods cannot guarantee that all tokens have sufficient receptive fields for global information inference. Thus, the impac… Show more
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