  We present a self-supervised learning framework, COCO-LM, that pretrains
Language Models by COrrecting and COntrasting corrupted text sequences.
Following ELECTRA-style pretraining, COCO-LM employs an auxiliary language
model to corrupt text sequences, upon which it constructs two new tasks for
pretraining the main model. The first token-level task, Corrective Language
Modeling, is to detect and correct tokens replaced by the auxiliary model, in
order to better capture token-level semantics. The second sequence-level task,
Sequence Contrastive Learning, is to align text sequences originated from the
same source input while ensuring uniformity in the representation space.
Experiments on GLUE and SQuAD demonstrate that COCO-LM not only outperforms
recent state-of-the-art pretrained models in accuracy, but also improves
pretraining efficiency. It achieves the MNLI accuracy of ELECTRA with 50% of
its pretraining GPU hours. With the same pretraining steps of standard
base/large-sized models, COCO-LM outperforms the previous best models by 1+
GLUE average points.
