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

Segmenting an address into meaningful components, also known as address
parsing, is an essential step in many applications from record linkage to
geocoding and package delivery. Consequently, a lot of work has been dedicated
to develop accurate address parsing techniques, with machine learning and
neural network methods leading the state-of-the-art scoreboard. However, most
of the work on address parsing has been confined to academic endeavours with
little availability of free and easy-to-use open-source solutions.
This paper presents Deepparse, a Python open-source, extendable, fine-tunable
address parsing solution under LGPL-3.0 licence to parse multinational
addresses using state-of-the-art deep learning algorithms and evaluated on over
60 countries. It can parse addresses written in any language and use any
address standard. The pre-trained model achieves average $99~\%$ parsing
accuracies on the countries used for training with no pre-processing nor
post-processing needed. Moreover, the library supports fine-tuning with new
data to generate a custom address parser.

这篇文章介绍了 Deepparse，它是一个 Python 开源、可扩展、可调优的地址解析解决方案，使用最先进的深度学习算法进行多国地址解析，并在 60 多个国家进行了评估。它可以解析任何语言和任何地址标准编写的地址。预训练模型在用于训练的国家上达到了平均 99% 的解析准确率，无需预处理或后处理。此外，该库支持使用新数据进行微调，生成自定义的地址解析器。