This paper presents the findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing. This first iteration of the shared task explores glossing of a set of six typologically diverse languages: Arapaho, Gitksan, Lezgi, Natügu, Tsez and Uspanteko. The shared task encompasses two tracks: a resource-scarce closed track and an open track, where participants are allowed to utilize external data resources. Five teams participated in the shared task. The winning team Tü-CL achieved a 23.99%-point improvement over a baseline RoBERTa system in the closed track and a 17.42%-point improvement in the open track.

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.