Creating a Universal Dependencies Treebank of Spoken Frisian-Dutch Code-switched Data

This paper explores the difficulties of annotating transcribed spoken Dutch-Frisian codeswitch utterances into Universal Dependencies. We make use of data from the FAME!
corpus, which consists of transcriptions and
audio data. Besides the usual annotation difficulties, this dataset is extra challenging because of Frisian being low-resource, the informal nature of the data, code-switching and
non-standard sentence segmentation. As a
starting point, two annotators annotated 150
random utterances in three stages of 50 utterances. After each stage, disagreements where
discussed and resolved. An increase of 7.8
UAS and 10.5 LAS points was achieved between the first and third round. This paper will
focus on the issues that arise when annotating
a transcribed speech corpus. To resolve these
issues several solutions are proposed.