  Pre-trained language models (PLM) have demonstrated their effectiveness for a
broad range of information retrieval and natural language processing tasks. As
the core part of PLM, multi-head self-attention is appealing for its ability to
jointly attend to information from different positions. However, researchers
have found that PLM always exhibits fixed attention patterns regardless of the
input (e.g., excessively paying attention to [CLS] or [SEP]), which we argue
might neglect important information in the other positions. In this work, we
propose a simple yet effective attention guiding mechanism to improve the
performance of PLM by encouraging attention towards the established goals.
Specifically, we propose two kinds of attention guiding methods, i.e., map
discrimination guiding (MDG) and attention pattern decorrelation guiding (PDG).
The former definitely encourages the diversity among multiple self-attention
heads to jointly attend to information from different representation subspaces,
while the latter encourages self-attention to attend to as many different
positions of the input as possible. We conduct experiments with multiple
general pre-trained models (i.e., BERT, ALBERT, and Roberta) and
domain-specific pre-trained models (i.e., BioBERT, ClinicalBERT, BlueBert, and
SciBERT) on three benchmark datasets (i.e., MultiNLI, MedNLI, and
Cross-genre-IR). Extensive experimental results demonstrate that our proposed
MDG and PDG bring stable performance improvements on all datasets with high
efficiency and low cost.
