Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning
This addresses the problem of inefficient fine-tuning for NLP practitioners by offering a more stable and data-efficient approach, though it is incremental as it builds on existing pre-training methods.
The paper tackles the sub-optimality of fine-tuning pre-trained transformers with random classifiers by introducing a scoring method that uses the pre-trained masked language modeling head for commonsense reasoning tasks, achieving up to 80% test accuracy on COPA and more stable training with less data.
Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. Most of the existing approaches rely on a randomly initialized classifier on top of such networks. We argue that this fine-tuning procedure is sub-optimal as the pre-trained model has no prior on the specific classifier labels, while it might have already learned an intrinsic textual representation of the task. In this paper, we introduce a new scoring method that casts a plausibility ranking task in a full-text format and leverages the masked language modeling head tuned during the pre-training phase. We study commonsense reasoning tasks where the model must rank a set of hypotheses given a premise, focusing on the COPA, Swag, HellaSwag and CommonsenseQA datasets. By exploiting our scoring method without fine-tuning, we are able to produce strong baselines (e.g. 80% test accuracy on COPA) that are comparable to supervised approaches. Moreover, when fine-tuning directly on the proposed scoring function, we show that our method provides a much more stable training phase across random restarts (e.g $\times 10$ standard deviation reduction on COPA test accuracy) and requires less annotated data than the standard classifier approach to reach equivalent performances.