Generating narratives from financial data using active learning

财务和业务分析师的主要职责之一是根据时间序列数据生成概述 ** 业务趋势的叙述性报告。就目前而言，这种分析通常是手动完成的，需要 ** 分析师倾倒大量数据以了解趋势，并执行特别分析以 ** 建立数据流之间的关系。因此，它目前是一个高度接触的过程，即使目前雇用了 ** 许多分析师，重要的趋势也可能在很长一段时间内未被发现。与其他领域一样，自动化有可能提高金融和商业分析领域的效率，提高分析速度，降低错误率，并将分析师从低级任务中解放出来，使他们能够专注于高级综合。自动化财务和业务分析任务的挑战之一是，分析师往往依赖多年的经验和特定领域的知识来实现良好的结果。因此，他们依赖于“完形”来引导他们的分析，这是很难用纯粹的基于规则的系统来复制的。因此，联合利华加拿大公司的目标是开发一种基于神经网络的系统，用于从数字时间序列数据中生成自然语言的叙述性报告，该系统将建立在人工智能子领域（如机器翻译，语音到文本，图像字幕和问题回答）的文本生成的最新进展基础上。

One of the major responsibilities of financial and business analysts is to generate narrative reports summarizing**business trends based on time series data. As it stands, this analysis is typically done manually, requiring**analysts to pour over huge amounts of data looking to understand trends, and performing ad hoc analyses to**establish relationships between data streams. As such, it is currently a high touch process and even with the**many analysts currently employed, important trends can go undetected for long periods of time. As in other**fields, a push toward automation has the potential to increase efficiency in the financial and business analysis**sector, increasing the speed of analysis, decreasing error rates, and freeing analysts from low-level tasks to**allow them to focus on high-level synthesis. One of the challenges in automating financial and business**analysis tasks is that analysts tend to rely on years of experience and domain specific knowledge to achieve**good results. Thus they rely on a "Gestalt" to lead their analysis, which is difficult to replicate using a pure**rule-based system. As such, Unilever Canada aims to develop a neural network based system for generating**narrative reports in natural language from numeric time series data, that will build on recent progress in text**generation from AI subfields such as machine translation, speech-to-text, image captioning and question**answering.