Accelerating scientific discovery using domain adaptive language modelling

We focus on but do not limit our work on patent corpora as methods to leverage such documents in discovery pipelines are limited so far. Our contributions focus on three specific cases: the domain definition of a given corpus in the form of a metagraph; the domain definition of a given corpus in the form of keywords, focusing on the patent classification case; and the semi-automated reporting of a discovery artifact in the form of a patent.

In all three cases, we rely on transformer-based Language Models and adhere to domain adaptive techniques to achieve our goals by providing efficient methods in terms of both performance and needed training/inference requirements. Concluding our work, we discuss the importance of our contributions. We demonstrate how our proposed methods can be incorporated into discovery pipelines, combined, and complement existing methods.

We conclude with a discussion of promising future directions derived from our work.<br><br>