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We present a new framework AMOS that pretrains text encoders with anAdversarial learning curriculum via a Mixture Of Signals from multipleauxiliary generators. Following ELECTRA-style pretraining, the main encoder istrained as a discriminator to detect replaced tokens generated by auxiliarymasked language models (MLMs). Different from ELECTRA which trains one MLM asthe generator, we jointly train multiple MLMs of different sizes to providetraining signals at various levels of difficulty. To push the discriminator tolearn better with challenging replaced tokens, we learn mixture weights overthe auxiliary MLMs' outputs to maximize the discriminator loss bybackpropagating the gradient from the discriminator via Gumbel-Softmax. Forbetter pretraining efficiency, we propose a way to assemble multiple MLMs intoone unified auxiliary model. AMOS outperforms ELECTRA and recentstate-of-the-art pretrained models by about 1 point on the GLUE benchmark forBERT base-sized models.

Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators