SICKNL: A Dataset for Dutch Natural Language Inference

Abstract

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.

Health information systems in intellectual disability care : Towards the re-use of routinely collected data

“…Until recently, the algorithms behind these techniques were mainly trained on English texts. Due to the enormous rise in research on AI algorithms, more of these techniques are also available for the Dutch language [243,244]. The Veracity of data may affect the result of the analysis done.…”