EU directives stipulate a systematic follow-up of train delays. In Sweden,
the Swedish Transport Administration registers and assigns an appropriate delay
attribution code. However, this delay attribution code is assigned manually,
which is a complex task. In this paper, a machine learning-based decision
support for assigning delay attribution codes based on event descriptions is
investigated. The text is transformed using TF-IDF, and two models, Random
Forest and Support Vector Machine, are evaluated against a random uniform
classifier and the classification performance of the Swedish Transport
Administration. Further, the problem is modeled as both a hierarchical and flat
approach. The results indicate that a hierarchical approach performs better
than a flat approach. Both approaches perform better than the random uniform
classifier but perform worse than the manual classification.

该研究旨在使用机器学习为基于事件描述的延误归因代码分配提供决策支持，并且结果表明分层方法比平面方法更有效，但均不如手工分类。

基于非结构化文本的列车管理系统的层次延迟归因分类

Hierarchical Delay Attribution Classification using Unstructured Text in  Train Management Systems

Major scandals in corporate history have urged the need for regulatory
compliance, where organizations need to ensure that their controls (processes)
comply with relevant laws, regulations, and policies. However, keeping track of
the constantly changing legislation is difficult, thus organizations are
increasingly adopting Regulatory Technology (RegTech) to facilitate the
process. To this end, we introduce regulatory information retrieval (REG-IR),
an application of document-to-document information retrieval (DOC2DOC IR),
where the query is an entire document making the task more challenging than
traditional IR where the queries are short. Furthermore, we compile and release
two datasets based on the relationships between EU directives and UK
legislation. We experiment on these datasets using a typical two-step pipeline
approach comprising a pre-fetcher and a neural re-ranker. Experimenting with
various pre-fetchers from BM25 to k nearest neighbors over representations from
several BERT models, we show that fine-tuning a BERT model on an in-domain
classification task produces the best representations for IR. We also show that
neural re-rankers under-perform due to contradicting supervision, i.e., similar
query-document pairs with opposite labels. Thus, they are biased towards the
pre-fetcher's score. Interestingly, applying a date filter further improves the
performance, showcasing the importance of the time dimension.

该研究介绍了一种基于文档检索的监管信息检索新方法 REG-IR，该方法使用一个包含整个文档的查询，比传统的短查询更具挑战性。此外，研究人员通过 EU 指令和英国立法之间的关系编制并发布了两个数据集，并使用基于 BERT 模型的多个表示从 BM25 到 k 最近邻的各种预取器进行了实验，证明对 BERT 模型进行领域内微调是 IR 的最佳表示。实验表明，由于存在相反标签的相似查询 - 文档对的相互矛盾监督，神经网络重新排序器表现不佳。然而，实验进一步表明，应用日期过滤器可以进一步提高检索性能，突显时间维度的重要性。