We present SICK-NL (read: signal), a dataset targeting Natural Language
Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of
Marelli et al. (2014)from English into Dutch. Having a parallel inference
dataset allows us to compare both monolingual and multilingual NLP models for
English and Dutch on the two tasks. In the paper, we motivate and detail the
translation process, perform a baseline evaluation on both the original SICK
dataset and its Dutch incarnation SICK-NL, taking inspiration from Dutch
skipgram embeddings and contextualised embedding models. In addition, we
encapsulate two phenomena encountered in the translation to formulate stress
tests and verify how well the Dutch models capture syntactic restructurings
that do not affect semantics. Our main finding is all models perform worse on
SICK-NL than on SICK, indicating that the Dutch dataset is more challenging
than the English original. Results on the stress tests show that models don't
fully capture word order freedom in Dutch, warranting future systematic
studies.

SICK-NL is presented as a challenging dataset for Natural Language Inference in Dutch, obtained by translating SICK dataset into Dutch and evaluating multilingual NLP models with a baseline evaluation, showing worse performance compared to the English original and lack of capturing word order freedom.