  We show-case an application of information extraction methods, such as named
entity recognition (NER) and relation extraction (RE) to a novel corpus,
consisting of documents, issued by a state agency. The main challenges of this
corpus are: 1) the annotation scheme differs greatly from the one used for the
general domain corpora, and 2) the documents are written in a language other
than English. Unlike expectations, the state-of-the-art transformer-based
models show modest performance for both tasks, either when approached
sequentially, or in an end-to-end fashion. Our experiments have demonstrated
that fine-tuning on a large unlabeled corpora does not automatically yield
significant improvement and thus we may conclude that more sophisticated
strategies of leveraging unlabelled texts are demanded. In this paper, we
describe the whole developed pipeline, starting from text annotation, baseline
development, and designing a shared task in hopes of improving the baseline.
Eventually, we realize that the current NER and RE technologies are far from
being mature and do not overcome so far challenges like ours.
