In the financial industry, identifying the location of parties involved in
payments is a major challenge in the context of various regulatory
requirements. For this purpose address parsing entails extracting fields such
as street, postal code, or country from free text message attributes. While
payment processing platforms are updating their standards with more structured
formats such as SWIFT with ISO 20022, address parsing remains essential for a
considerable volume of messages. With the emergence of Transformers and
Generative Large Language Models (LLM), we explore the performance of
state-of-the-art solutions given the constraint of processing a vast amount of
daily data. This paper also aims to show the need for training robust models
capable of dealing with real-world noisy transactional data. Our results
suggest that a well fine-tuned Transformer model using early-stopping
significantly outperforms other approaches. Nevertheless, generative LLMs
demonstrate strong zero-shot performance and warrant further investigations.

在金融行业中，从自由文本消息属性中提取街道、邮政编码或国家等字段是解析地址的关键需求，本文利用 Transformer 模型和生成型大语言模型探索应对海量交易数据的性能，并展示训练鲁棒模型处理实际世界的嘈杂交易数据的重要性。结果表明，良好调整的 Transformer 模型通过提前停止显著优于其他方法，而生成型大语言模型则展现出强大的零 - shot 性能，值得进一步研究。

用 Transformers 打击犯罪：在支付数据中进行地址解析方法的经验分析

Fighting crime with Transformers: Empirical analysis of address parsing  methods in payment data

Tabular data is a crucial form of information expression, which can organize
data in a standard structure for easy information retrieval and comparison.
However, in financial industry and many other fields tables are often disclosed
in unstructured digital files, e.g. Portable Document Format (PDF) and images,
which are difficult to be extracted directly. In this paper, to facilitate deep
learning based table extraction from unstructured digital files, we publish a
standard Chinese dataset named FinTab, which contains more than 1,600 financial
tables of diverse kinds and their corresponding structure representation in
JSON. In addition, we propose a novel graph-based convolutional neural network
model named GFTE as a baseline for future comparison. GFTE integrates image
feature, position feature and textual feature together for precise edge
prediction and reaches overall good results.

本文提出了 FinTab 数据集，包含 1600 个财务表以及它们在 JSON 表示中的结构，以方便从非结构化数字文件中提取表格。同时，作者还提出了一种基于 GFTE 的图卷积神经网络模型，它将图像特征、位置特征和文本特征集成在一起，用于精确的边缘预测，并取得了总体上不错的结果。