R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason
Naoya Inoue, Pontus Stenetorp, Kentaro Inui
Abstract
Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This prevents the community from reliably measuring the progress of RC systems. To address this issue, we introduce R 4 C, a new task for evaluating RC systems' internal reasoning. R 4 C requires giving not only answers but also derivations: explanations that justify predicted answers. We present a reliable, crowdsourced framework for scalably annotating RC datasets with derivations. We create and publicly release the R 4 C dataset, the first, quality-assured dataset consisting of 4.6k questions, each of which is annotated with 3 reference derivations (i.e. 13.8k derivations). Experiments show that our automatic evaluation metrics using multiple reference derivations are reliable, and that R 4 C assesses different skills from an existing benchmark.