A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline, requirements often change, with classes being added and removed, which leads to nontrivial modifications to the source code and the possible introduction of bugs. In this work, we evaluate sequence-to-sequence models as an alternative to token-level classification methods for information extraction of legal and registration documents. We finetune models that jointly extract the information and generate the output already in a structured format. Post-processing steps are learned during training, thus eliminating the need for rule-based methods and simplifying the pipeline. Furthermore, we propose a novel method to align the output with the input text, thus facilitating system inspection and auditing. Our experiments on four real-world datasets show that the proposed method is an alternative to classical pipelines.

本文研究使用序列到序列模型作为信息提取的替代方案，针对法律和注册文件等领域的文本，采用微调模型以在结构化格式输出的同时提取信息，学习后处理步骤以消除基于规则的方法，精简流程。我们提出了一种新的方法来对齐输出和输入文本，证明该方法是传统流程的一种替代方案。实验结果表明，该方法适用于四个真实世界的数据集。

从注册和法律文件中提取信息的序列到序列模型