  Most available semantic parsing datasets, comprising of pairs of natural
utterances and logical forms, were collected solely for the purpose of training
and evaluation of natural language understanding systems. As a result, they do
not contain any of the richness and variety of natural-occurring utterances,
where humans ask about data they need or are curious about. In this work, we
release SEDE, a dataset with 12,023 pairs of utterances and SQL queries
collected from real usage on the Stack Exchange website. We show that these
pairs contain a variety of real-world challenges which were rarely reflected so
far in any other semantic parsing dataset, propose an evaluation metric based
on comparison of partial query clauses that is more suitable for real-world
queries, and conduct experiments with strong baselines, showing a large gap
between the performance on SEDE compared to other common datasets.

Computation and Language