  Leaderboards have eased model development for many NLP datasets by
standardizing their evaluation and delegating it to an independent external
repository. Their adoption, however, is so far limited to tasks that can be
reliably evaluated in an automatic manner. This work introduces GENIE, an
extensible human evaluation leaderboard, which brings the ease of leaderboards
to text generation tasks. GENIE automatically posts leaderboard submissions to
crowdsourcing platforms asking human annotators to evaluate them on various
axes (e.g., correctness, conciseness, fluency) and compares their answers to
various automatic metrics. We introduce several datasets in English to GENIE,
representing four core challenges in text generation: machine translation,
summarization, commonsense reasoning, and machine comprehension. We provide
formal granular evaluation metrics and identify areas for future research. We
make GENIE publicly available and hope that it will spur progress in language
generation models as well as their automatic and manual evaluation.
