  The correct use of Dutch pronouns 'die' and 'dat' is a stumbling block for
both native and non-native speakers of Dutch due to the multiplicity of
syntactic functions and the dependency on the antecedent's gender and number.
Drawing on previous research conducted on neural context-dependent dt-mistake
correction models (Heyman et al. 2018), this study constructs the first neural
network model for Dutch demonstrative and relative pronoun resolution that
specifically focuses on the correction and part-of-speech prediction of these
two pronouns. Two separate datasets are built with sentences obtained from,
respectively, the Dutch Europarl corpus (Koehn 2015) - which contains the
proceedings of the European Parliament from 1996 to the present - and the SoNaR
corpus (Oostdijk et al. 2013) - which contains Dutch texts from a variety of
domains such as newspapers, blogs and legal texts. Firstly, a binary
classification model solely predicts the correct 'die' or 'dat'. The classifier
with a bidirectional long short-term memory architecture achieves 84.56%
accuracy. Secondly, a multitask classification model simultaneously predicts
the correct 'die' or 'dat' and its part-of-speech tag. The model containing a
combination of a sentence and context encoder with both a bidirectional long
short-term memory architecture results in 88.63% accuracy for die/dat
prediction and 87.73% accuracy for part-of-speech prediction. More
evenly-balanced data, larger word embeddings, an extra bidirectional long
short-term memory layer and integrated part-of-speech knowledge positively
affects die/dat prediction performance, while a context encoder architecture
raises part-of-speech prediction performance. This study shows promising
results and can serve as a starting point for future research on machine
learning models for Dutch anaphora resolution.
