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根据时间序列数据生成总结业务趋势的叙述性报告。目前，这种分析通常是手工完成的，需要分析人员对大量数据进行分析以了解趋势，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，即使有许多目前雇用的分析师，重要的趋势也可能在很长一段时间内未被发现。正如在其他领域一样，推动自动化有可能提高金融和业务分析部门的效率，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倾向于依赖多年的经验和特定领域的知识来获得良好的结果。因此，他们依靠“格式塔”来引导他们的分析，这很难用纯粹的基于规则的系统来复制。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，这将建立在人工智能子领域（如机器翻译、语音到文本、图像字幕和问答）文本生成的最新进展上。