On Losses for Modern Language Models

• URL: http://arxiv.org/abs/2010.01694v1
• Date: Sun, 4 Oct 2020 21:44:15 GMT
• Title: On Losses for Modern Language Models
• Authors: Stephane Aroca-Ouellette, Frank Rudzicz
• Abstract summary: We show that NSP is detrimental to training due to its context splitting and shallow semantic signal. Using multiple tasks in a multi-task pre-training framework provides better results than using any single auxiliary task.
• Score: 18.56205816291398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: BERT set many state-of-the-art results over varied NLU benchmarks by pre-training over two tasks: masked language modelling (MLM) and next sentence prediction (NSP), the latter of which has been highly criticized. In this paper, we 1) clarify NSP's effect on BERT pre-training, 2) explore fourteen possible auxiliary pre-training tasks, of which seven are novel to modern language models, and 3) investigate different ways to include multiple tasks into pre-training. We show that NSP is detrimental to training due to its context splitting and shallow semantic signal. We also identify six auxiliary pre-training tasks -- sentence ordering, adjacent sentence prediction, TF prediction, TF-IDF prediction, a FastSent variant, and a Quick Thoughts variant -- that outperform a pure MLM baseline. Finally, we demonstrate that using multiple tasks in a multi-task pre-training framework provides better results than using any single auxiliary task. Using these methods, we outperform BERT Base on the GLUE benchmark using fewer than a quarter of the training tokens.

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