New INSPIRED dataset for transparent interactive semantic parsing

We’re releasing a new dataset, INSPIRED, along with our ACL-22 Findings paper, Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction. The paper documents the many steps we took to obtain a high-quality dataset of crowdsourced paraphrases intended to spur progress in research on interactive semantic parsing, with the ultimate aim of enabling users to obtain answers to complex natural language questions from knowledge bases with high confidence. Analyses of baseline models show the benefit of taking context into account and the potential for user interaction to enable much higher task success.