Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pre-training task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute.
Pre-training Language Model as a Multi-perspective Course Learner
Microsoft Corporation（微软公司）；National Engineering Research Center of Speech and Language Information Processing（国家语音及语言信息处理工程技术研究中心）；University of Science and Technology of China（中国科学技术大学）
Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation
Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；Element AI（Element AI公司）；Center for Data Science, New York University（纽约大学数据科学中心）；Facebook AI Research, Facebook Inc.（Facebook公司Facebook人工智能研究院）
