The European Patent Office (EPO) is an international organisation responsible for granting patents and promoting global cooperation in the intellectual property world. With three official languages (English, German, French) and a need to constantly access and manipulate information in multiple languages, machine translation is essential for the EPO. Over the last years we have developed internal machine translation engines, specifically for the translation of patent language. This article presents our data generation strategy: it describes our approach to the generation of parallel corpora of documents, training datasets of aligned sentences, and respective evaluation datasets. Details on the challenges and technical implementation are presented, as well as statistics of the training dataset generation process.

Fully characterizing structural and functional sites in proteins is a fundamental step in understanding their roles in the cell. This extremely challenging combinatorial problem requires determining the number of sites in the protein and the set of residues involved in each of them. We formulate it as a distance-based supervised clustering task, where training proteins are employed to learn a proper distance function between residues. A partial clustering is then returned by searching for maximum-weight cliques in the resulting weighted graph representation of proteins. A novel stochastic local search algorithm is proposed to efficiently generate approximate solutions. Our method achieves substantial improvements over a previous structured-output approach for metal binding site prediction. Significant improvements over the current state-of-the-art are also achieved in predicting catalytic sites from 3D structure in enzymes.