Semantic role labeling (SRL) resources, such as Proposition Bank (PropBank), provide useful input to downstream applications. In this paper we present some challenges and insights we learned while expanding the previously developed Russian PropBank. This new effort involved annotation and adjudication of all predicates within a subset of the prior work in order to provide a test corpus for future applications. We discuss a number of new issues that arose while developing our PropBank for Russian as well as our solutions. Framing issues include: distinguishing between morphological processes that warrant new frames, differentiating between modal verbs and predicate verbs, and maintaining accurate representations of a given language’s semantics. Annotation issues include disagreements derived from variability in Universal Dependency parses and semantic ambiguity within the text. Finally, we demonstrate how Russian sentence structures reveal inherent limitations to PropBank’s ability to capture semantic data. These discussions should prove useful to anyone developing a PropBank or similar SRL resources for a new language.

This paper describes our submission to SemEval-2023 Task 6, Subtask B, a shared task on performing Named Entity Recognition in legal documents for specific legal entity types. Documents are divided into the preamble and judgement texts, and certain entity types should only be tagged in one of the two text sections. To address this challenge, our team proposes a token classification model that is augmented with information about the document type, which achieves greater performance than the non-augmented system.