  Large pre-trained language models have recently enabled open-ended generation
frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond
the traditional data-to-text generation. While this framework is more general,
it is under-specified and often leads to a lack of controllability restricting
their real-world usage. We propose a new grounded keys-to-text generation task:
the task is to generate a factual description about an entity given a set of
guiding keys, and grounding passages. To address this task, we introduce a new
dataset, called EntDeGen. Inspired by recent QA-based evaluation measures, we
propose an automatic metric, MAFE, for factual correctness of generated
descriptions. Our EntDescriptor model is equipped with strong rankers to fetch
helpful passages and generate entity descriptions. Experimental result shows a
good correlation (60.14) between our proposed metric and human judgments of
factuality. Our rankers significantly improved the factual correctness of
generated descriptions (15.95% and 34.51% relative gains in recall and
precision). Finally, our ablation study highlights the benefit of combining
keys and groundings.
