Pre-training Language Model as a Multi-perspective Course Learner
Beiduo Chen Shaohan Huang Zihan Zhang Wu Guo Zhenhua Ling Haizhen Huang Furu Wei Weiwei Deng Qi Zhang
ELECTRA, the generator-discriminator pre-training framework, has achieved
impressive semantic construction capability among various downstream tasks.
Despite the convincing performance, ELECTRA still faces the challenges of
monotonous training and deficient interaction. Generator with only masked
language modeling (MLM) leads to biased learning and label imbalance for
discriminator, decreasing learning efficiency; no explicit feedback loop from
discriminator to generator results in the chasm between these two components,
underutilizing the course learning. In this study, a multi-perspective course
learning (MCL) method is proposed to fetch a many degrees and visual angles for
sample-efficient pre-training, and to fully leverage the relationship between
generator and discriminator. Concretely, three self-supervision courses are
designed to alleviate inherent flaws of MLM and balance the label in a
multi-perspective way. Besides, two self-correction courses are proposed to
bridge the chasm between the two encoders by creating a "correction notebook"
for secondary-supervision. Moreover, a course soups trial is conducted to solve
the "tug-of-war" dynamics problem of MCL, evolving a stronger pre-trained
model. Experimental results show that our method significantly improves
ELECTRA's average performance by 2.8% and 3.2% absolute points respectively on
GLUE and SQuAD 2.0 benchmarks, and overshadows recent advanced ELECTRA-style
models under the same settings. The pre-trained MCL model is available at
https://huggingface.co/McmanusChen/MCL-base.