Challenges in Annotating and Parsing Spoken, Code-switched, Frisian-Dutch Data

Rob van der Goot, Anouck Braggaar

Abstract:

While high performance has been obtained
for dependency parsing of high-resource languages, performance for low-resource languages lags behind. In this paper we focus
on the parsing of the low-resource language
Frisian. We use a sample of code-switched,
spontaneously spoken data, which proves to
be a challenging setup. We propose to train
a parser specifically tailored towards the target
domain, by selecting instances from multiple
treebanks. Specifically, we use Latent Dirichlet Allocation (LDA), with word and character N-gram features. The best single source
treebank (NL ALPINO) resulted in an LAS of
54.7 whereas our data selection outperformed
the single best transfer treebank and led to
55.6 LAS on the test data. Additional experiments consisted of removing diacritics from
our Frisian data, creating more similar training
data by cropping sentences and running our
best model using XLM-R. These experiments
did not lead to a better performance.