  We analyze two Natural Language Inference data sets with respect to their
linguistic features. The goal is to identify those syntactic and semantic
properties that are particularly hard to comprehend for a machine learning
model. To this end, we also investigate the differences between a
crowd-sourced, machine-translated data set (SNLI) and a collection of text
pairs from internet sources. Our main findings are, that the model has
difficulty recognizing the semantic importance of prepositions and verbs,
emphasizing the importance of linguistically aware pre-training tasks.
Furthermore, it often does not comprehend antonyms and homonyms, especially if
those are depending on the context. Incomplete sentences are another problem,
as well as longer paragraphs and rare words or phrases. The study shows that
automated language understanding requires a more informed approach, utilizing
as much external knowledge as possible throughout the training process.
