  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.
