âHellaSwag: Can a Machine Really Finish Your Sentence?â, 2019-05-19 (; backlinks; similar)â :
Recent work by et al 2018 introduced a new task of commonsense natural language inference: given an event description such as âA woman sits at a pianoâ, a machine must select the most likely followup: âShe sets her fingers on the keys.â With the introduction of BERT, near human-level performance was reached. Does this mean that machines can perform human level commonsense inference?
In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset. Though its questions are trivial for humans (>95% accuracy), state-of-the-art models struggle (<48%).
We achieve this via Adversarial Filtering (AF), a data collection paradigm wherein a series of discriminators iteratively select an adversarial set of machine-generated wrong answers. AF proves to be surprisingly robust. The key insight is to scale up the length and complexity of the dataset examples towards a critical âGoldilocksâ zone wherein generated text is ridiculous to humans, yet often misclassified by state-of-the-art models.
Our construction of HellaSwag, and its resulting difficulty, sheds light on the inner workings of deep pretrained models. More broadly, it suggests a new path forward for NLP research, in which benchmarks co-evolve with the evolving state-of-the-art in an adversarial way, so as to present ever-harder challenges.