Patents show how technology evolves in most scientific fields over time.
The best way to use this valuable knowledge base is to use efficient and
effective information retrieval and searches for related prior art.
Patent classification, i.e., assigning a patent to one or more
predefined categories, is a fundamental step towards synthesizing the
information content of an invention. To this end, architectures based on
Transformers, especially those derived from the BERT family have already
been proposed in the literature and they have shown remarkable results
by setting a new state-of-the-art performance for the classification
task. Here, we study how domain adaptation can push the performance
boundaries in patent classification by rigorously evaluating and
implementing a collection of recent transfer learning techniques, e.g.,
domain-adaptive pretraining and adapters. Our analysis shows how
leveraging these advancements enables the development of
state-of-the-art models with increased precision, recall, and F1-score.
We base our evaluation on both standard patent classification datasets
derived from patent offices-defined code hierarchies and more practical
real-world use-case scenarios containing labels from the agrochemical
industrial domain. The application of these domain adapted techniques to
patent classification in a multilingual setting is also examined and
evaluated.