Toward a Comprehension Challenge, Using Crowdsourcing as a Tool

Human readers comprehend vastly more, and in vastly different ways, than any existing comprehension test would suggest. An ideal comprehension test for a story should cover the full range of questions and answers that humans would expect other humans to reasonably learn or infer from a given story. ICCG uses structured crowdsourcing to comprehensively generate relevant questions and supported answers for arbitrary stories, whether fiction or nonfiction, presented across a variety of media such as videos, podcasts, and still images. While the AI scientific community had hoped that by 2015 machines would be able to read and comprehend language, current models are typically superficial, capable of understanding sentences in limited domains (such as extracting movie times and restaurant locations from text) but without the sort of widecoverage comprehension that we expect of any teenager. Comprehension itself extends beyond the written word; most adults and children can comprehend a variety of narratives, both fiction and nonfiction, presented in a wide variety of formats, such as movies, television and radio programs, written stories, YouTube videos, still images, and cartoons.