  Large pre-trained sentence encoders like BERT start a new chapter in natural
language processing. A common practice to apply pre-trained BERT to sequence
classification tasks (e.g., classification of sentences or sentence pairs) is
by feeding the embedding of [CLS] token (in the last layer) to a task-specific
classification layer, and then fine tune the model parameters of BERT and
classifier jointly. In this paper, we conduct systematic analysis over several
sequence classification datasets to examine the embedding values of [CLS] token
before the fine tuning phase, and present the biased embedding distribution
issue---i.e., embedding values of [CLS] concentrate on a few dimensions and are
non-zero centered. Such biased embedding brings challenge to the optimization
process during fine-tuning as gradients of [CLS] embedding may explode and
result in degraded model performance. We further propose several simple yet
effective normalization methods to modify the [CLS] embedding during the
fine-tuning. Compared with the previous practice, neural classification model
with the normalized embedding shows improvements on several text classification
tasks, demonstrates the effectiveness of our method.
