The field of natural language processing (NLP) has grown over the last few years: conferences have become larger, we have published an incredible amount of papers, and state-of-the-art research has been implemented in a large variety of customer-facing products. However, this paper argues that we have been less successful than we should have been and reflects on where and how the field fails to tap its full potential. Specifically, we demonstrate that, in recent years, subpar time allocation has been a major obstacle for NLP research. We outline multiple concrete problems together with their negative consequences and, importantly, suggest remedies to improve the status quo. We hope that this paper will be a starting point for discussions around which common practices are -- or are not -- beneficial for NLP research.

尽管对视觉识别任务进行了显着进展，但是当培训数据稀缺或高度不平衡时，深神经网络仍然易于普遍，使他们非常容易受到现实世界的例子。在本文中，我们提出了一种令人惊讶的简单且高效的方法来缓解此限制：使用纯噪声图像作为额外的训练数据。与常见使用添加剂噪声或对抗数据的噪声不同，我们通过直接训练纯无随机噪声图像提出了完全不同的视角。我们提出了一种新的分发感知路由批量归一化层（DAR-BN），除了同一网络内的自然图像之外，还可以在纯噪声图像上训练。这鼓励泛化和抑制过度装备。我们所提出的方法显着提高了不平衡的分类性能，从而获得了最先进的导致大量的长尾图像分类数据集（Cifar-10-LT，CiFar-100-LT，想象齿 - LT，和celeba-5）。此外，我们的方法非常简单且易于使用作为一般的新增强工具（在现有增强的顶部），并且可以在任何训练方案中结合。它不需要任何专门的数据生成或培训程序，从而保持培训快速高效