  Hard interaction learning between source sequences and their next targets is
challenging, which exists in a myriad of sequential prediction tasks. During
the training process, most existing methods focus on explicitly hard
interactions caused by wrong responses. However, a model might conduct correct
responses by capturing a subset of learnable patterns, which results in
implicitly hard interactions with some unlearned patterns. As such, its
generalization performance is weakened. The problem gets more serious in
sequential prediction due to the interference of substantial similar candidate
targets.
  To this end, we propose a Hardness Aware Interaction Learning framework
(HAIL) that mainly consists of two base sequential learning networks and mutual
exclusivity distillation (MED). The base networks are initialized differently
to learn distinctive view patterns, thus gaining different training
experiences. The experiences in the form of the unlikelihood of correct
responses are drawn from each other by MED, which provides mutual exclusivity
knowledge to figure out implicitly hard interactions. Moreover, we deduce that
the unlikelihood essentially introduces additional gradients to push the
pattern learning of correct responses. Our framework can be easily extended to
more peer base networks. Evaluation is conducted on four datasets covering
cyber and physical spaces. The experimental results demonstrate that our
framework outperforms several state-of-the-art methods in terms of top-k based
metrics.
