论文标题
自然语言处理研究深度学习的实验标准
Experimental Standards for Deep Learning in Natural Language Processing Research
论文作者
论文摘要
在过去的十年中，深度学习领域（DL）经历了爆炸性的增长，对自然语言处理（NLP）也产生了重大影响。然而，与更具成熟的学科相比，缺乏常见的实验标准仍然是对整个领域的开放挑战。从基本的科学原理开始，我们将关于NLP实验标准的持续讨论提炼成一种可公开的方法。遵循这些最佳实践对于加强实验证据，提高可重复性并支持科学进步至关重要。这些标准进一步收集到公共存储库中，以帮助他们透明地适应未来的需求。
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, compared to more established disciplines, a lack of common experimental standards remains an open challenge to the field at large. Starting from fundamental scientific principles, we distill ongoing discussions on experimental standards in NLP into a single, widely-applicable methodology. Following these best practices is crucial to strengthen experimental evidence, improve reproducibility and support scientific progress. These standards are further collected in a public repository to help them transparently adapt to future needs.
