基于NLP的德国中央银行证券发行资格审查决策支持系统
NLP-based Decision Support System for Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
摘要：
作为其数字化计划的一部分，德国中央银行（德意志联邦银行）希望研究自然语言处理（NLP）在多大程度上可用于对证券招股说明书的资格标准做出独立决策。每个月，德国中央银行的市场总监都会收到数百份PDF格式的招股说明书扫描件，这些招股说明书必须经过人工处理才能确定其资格。我们发现，这个繁琐而耗时的过程可以通过采用现代NLP模型架构来实现（半）自动化，该架构学习文本中的语言特征表示，以识别当前合格和不合格的标准。拟议的决策支持系统提供了文件级资格标准的决定，并附有对决定的人类可理解的解释。该项目的目的是对所描述的用例进行建模，并评估NLP领域当前研究成果可以应用于此问题的程度。在创建了一个包含相关标准的合格和不合格提及的注释的异构领域特定数据集之后，我们能够成功地构建、训练和部署半自动决策器模型。该模型是基于转换器的语言模型和决策树，它集成了决策过程中建立的基于规则的部分。结果表明，有可能有效地模拟问题和自动化决策超过90%的许多考虑的资格标准。
英文摘要：
As part of its digitization initiative, the German Central Bank (Deutsche Bundesbank) wants to examine the extent to which natural Language Processing (NLP) can be used to make independent decisions upon the eligibility criteria of securities prospectuses. Every month, the Directorate General Markets at the German Central Bank receives hundreds of scanned prospectuses in PDF format, which must be manually processed to decide upon their eligibility. We found that this tedious and time-consuming process can be (semi-)automated by employing modern NLP model architectures, which learn the linguistic feature representation in text to identify the present eligible and ineligible criteria. The proposed Decision Support System provides decisions of document-level eligibility criteria accompanied by human-understandable explanations of the decisions. The aim of this project is to model the described use case and to evaluate the extent to which current research results from the field of NLP can be applied to this problem. After creating a heterogeneous domain-specific dataset containing annotations of eligible and non-eligible mentions of relevant criteria, we were able to successfully build, train and deploy a semi-automatic decider model. This model is based on transformer-based language models and decision trees, which integrate the established rule-based parts of the decision processes. Results suggest that it is possible to efficiently model the problem and automate decision making to more than 90% for many of the considered eligibility criteria.
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