The Federal Reserve System (the Fed) plays a significant role in affecting monetary policy and financial conditions worldwide. Although it is important to analyse the Fed's communications to extract useful information, it is generally long-form and complex due to the ambiguous and esoteric nature of content. In this paper, we present FedNLP, an interpretable multi-component Natural Language Processing system to decode Federal Reserve communications. This system is designed for end-users to explore how NLP techniques can assist their holistic understanding of the Fed's communications with NO coding. Behind the scenes, FedNLP uses multiple NLP models from traditional machine learning algorithms to deep neural network architectures in each downstream task. The demonstration shows multiple results at once including sentiment analysis, summary of the document, prediction of the Federal Funds Rate movement and visualization for interpreting the prediction model's result.
翻译:美联储储备体系(美联储)在影响全球货币政策和金融条件方面发挥着重要作用。尽管分析美联储的通信以获取有用信息十分重要,但由于内容的模糊性和隐秘性,它一般是长式和复杂的。在本文中,我们介绍了一个可解释的多种组成部分的美联储语言处理系统(FedNLP),用于解码美联储的通信。这个系统是为终端用户设计的,以探索国家储备体系技术如何帮助他们全面理解美联储与NO编码的通信。在幕后,美联储将传统机器学习算法的多个NLP模型用于每个下游任务中的深层神经网络结构。演示显示了一次的多重结果,包括情绪分析、文件摘要、联邦基金利率变化预测以及解释预测模型结果的可视化。