What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge

Understand

Open-domain question answering (QA) is known to involve several underlying knowledge and reasoning challenges, but are models actually learning such knowledge when trained on benchmark tasks? To investigate this, we introduce several new challenge tasks that probe whether state-of-the-art QA models have general knowledge about word definitions and general taxonomic reasoning, both of which are fundamental to more complex forms of reasoning and are widespread in benchmark datasets.

• As an alternative to expensive crowd-sourcing, we introduce a methodology for automatically building datasets from various types of expert knowledge (e.g., knowledge graphs and lexical taxonomies), allowing for systematic control over the resulting probes and for a more comprehensive evaluation.
• We find automatically constructing probes to be vulnerable to annotation artifacts, which we carefully control for.
• Our evaluation confirms that transformer-based QA models are already predisposed to recognize certain types of structural lexical knowledge.