  Prior studies diagnose the anisotropy problem in sentence representations
from pre-trained language models, e.g., BERT, without fine-tuning. Our analysis
reveals that the sentence embeddings from BERT suffer from a bias towards
uninformative words, limiting the performance in semantic textual similarity
(STS) tasks. To address this bias, we propose a simple and efficient
unsupervised approach, Diagonal Attention Pooling (Ditto), which weights words
with model-based importance estimations and computes the weighted average of
word representations from pre-trained models as sentence embeddings. Ditto can
be easily applied to any pre-trained language model as a postprocessing
operation. Compared to prior sentence embedding approaches, Ditto does not add
parameters nor requires any learning. Empirical evaluations demonstrate that
our proposed Ditto can alleviate the anisotropy problem and improve various
pre-trained models on STS tasks.
