We introduce Self-CRItic Pretraining Transformers (SCRIPT) for representation learning of text. The popular masked language modeling (MLM) pretraining methods like BERT replace some tokens with [MASK] and an encoder is trained to recover them, while
ELECTRA trains a discriminator to detect replaced tokens proposed by a generator. In contrast, we train a language model as in MLM and further derive a discriminator or critic on top of the encoder without using any additional parameters. That is, the model itself is a critic. SCRIPT combines MLM training and discriminative training for learning rich representations and compute- and sample-efficiency. We demonstrate improved sample-efficiency in pretraining and enhanced representations evidenced by improved downstream task performance on GLUE and SQuAD over strong baselines. Also, the self-critic scores can be directly used as pseudo-log-likelihood for efficient scoring.
Religion has been one of the most important phenomena of human culture, which has
gained the deep attention of Nietzsche. In view of this, our research has sought to delve
into the depth of the Nietzsche’s critique of religion, which seeks to revea
l the source or
origin of the religion and the extent of its stiffness or Triviality. The interpretation methods
has relied on revealing the value of interpretations given by the sponsors of religions in
different concepts and their consequences, shedding light on the nature and the goals of
the wills that control them.
