  Masked language modeling (MLM), a self-supervised pretraining objective, is
widely used in natural language processing for learning text representations.
MLM trains a model to predict a random sample of input tokens that have been
replaced by a [MASK] placeholder in a multi-class setting over the entire
vocabulary. When pretraining, it is common to use alongside MLM other auxiliary
objectives on the token or sequence level to improve downstream performance
(e.g. next sentence prediction). However, no previous work so far has attempted
in examining whether other simpler linguistically intuitive or not objectives
can be used standalone as main pretraining objectives. In this paper, we
explore five simple pretraining objectives based on token-level classification
tasks as replacements of MLM. Empirical results on GLUE and SQuAD show that our
proposed methods achieve comparable or better performance to MLM using a
BERT-BASE architecture. We further validate our methods using smaller models,
showing that pretraining a model with 41% of the BERT-BASE's parameters,
BERT-MEDIUM results in only a 1% drop in GLUE scores with our best objective.
