论文标题
为您的业务采用NLP时要考虑的费用
Costs to Consider in Adopting NLP for Your Business
论文作者
论文摘要
自然语言处理（NLP）的最新进展在很大程度上将基于变压器的模型推向了最新的技术，而无需过多考虑生产和利用成本。由于缺乏机器，数据和人力资源来构建这些方法，计划将这些方法采用这些方法的公司面临困难。我们将经典学习算法的性能和成本与最新的序列和文本标签任务进行比较。在我们的工业数据集中，我们发现尽管成本较低，但经典模型通常与深层神经相提并论。我们展示了绩效增长与整个模型成本之间的权衡，以提供更多有关AI-Pivoting业务的见解。此外，我们呼吁对低成本模型进行更多研究，尤其是对于资源不足的语言。
Recent advances in Natural Language Processing (NLP) have largely pushed deep transformer-based models as the go-to state-of-the-art technique without much regard to the production and utilization cost. Companies planning to adopt these methods into their business face difficulties because of the lack of machine, data, and human resources to build them. We compare both the performance and the cost of classical learning algorithms to the latest ones in common sequence and text labeling tasks. In our industrial datasets, we find that classical models often perform on par with deep neural ones despite the lower cost. We show the trade-off between performance gain and the cost across the models to give more insights for AI-pivoting business. Further, we call for more research into low-cost models, especially for under-resourced languages.
