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We introduce a set of nine challenge tasks that test for the understanding offunction words. These tasks are created by structurally mutating sentences fromexisting datasets to target the comprehension of specific types of functionwords (e.g., prepositions, wh-words). Using these probing tasks, we explore theeffects of various pretraining objectives for sentence encoders (e.g., languagemodeling, CCG supertagging and natural language inference (NLI)) on the learnedrepresentations. Our results show that pretraining on language modelingperforms the best on average across our probing tasks, supporting itswidespread use for pretraining state-of-the-art NLP models, and CCGsupertagging and NLI pretraining perform comparably. Overall, no pretrainingobjective dominates across the board, and our function word probing taskshighlight several intuitive differences between pretraining objectives, e.g.,that NLI helps the comprehension of negation.

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension