  One reason pretraining on self-supervised linguistic tasks is effective is
that it teaches models features that are helpful for language understanding.
However, we want pretrained models to learn not only to represent linguistic
features, but also to use those features preferentially during fine-turning.
With this goal in mind, we introduce a new English-language diagnostic set
called MSGS (the Mixed Signals Generalization Set), which consists of 20
ambiguous binary classification tasks that we use to test whether a pretrained
model prefers linguistic or surface generalizations during fine-tuning. We
pretrain RoBERTa models from scratch on quantities of data ranging from 1M to
1B words and compare their performance on MSGS to the publicly available
RoBERTa-base. We find that models can learn to represent linguistic features
with little pretraining data, but require far more data to learn to prefer
linguistic generalizations over surface ones. Eventually, with about 30B words
of pretraining data, RoBERTa-base does demonstrate a linguistic bias with some
regularity. We conclude that while self-supervised pretraining is an effective
way to learn helpful inductive biases, there is likely room to improve the rate
at which models learn which features matter.
