Generating narratives from financial data using active learning

使用主动学习从财务数据中生成叙述

• 批准号: 531066-2018
• 负责人: Bener, Ayse
• 金额: $ 1.82万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=645141
• 关键词: Generating; narratives; financial; data; using

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.

财务和业务分析师的主要职责之一是生成叙事报告，总结基于时间序列数据的业务趋势。就目前而言，该分析通常是手动进行的，要求**分析师倾注大量的数据以了解趋势，并进行临时分析以**建立数据流之间的关系。因此，目前这是一个高触摸过程，即使**许多分析师目前使用的是，重要的趋势也可能长期未被发现。与其他**领域一样，朝着自动化的推动可能会提高财务和业务分析的效率**行业，提高分析速度，降低错误率以及使分析师从低级任务释放到**允许他们专注于高级合成。自动化财务和业务**分析任务的挑战之一是，分析师倾向于依靠多年的经验和领域特定的知识来取得好成绩。因此，他们依靠“格式塔”来领导他们的分析，这很难使用基于**规则的系统复制。因此，联合利华加拿大旨在开发基于神经网络的系统，以从数字时间序列数据中生成**自然语言的叙事报告，该数据将基于本文本**的最新进展**从AI子字段（例如机器翻译，语音到文本，图像字幕和问题**答案）中产生的文本**。