  To translate natural language questions into executable database queries,
most approaches rely on a fully annotated training set. Annotating a large
dataset with queries is difficult as it requires query-language expertise. We
reduce this burden using grounded in databases intermediate question
representations. These representations are simpler to collect and were
originally crowdsourced within the Break dataset (Wolfson et al., 2020). Our
pipeline consists of two parts: a neural semantic parser that converts natural
language questions into the intermediate representations and a non-trainable
transpiler to the SPARQL query language (a standard language for accessing
knowledge graphs and semantic web). We chose SPARQL because its queries are
structurally closer to our intermediate representations (compared to SQL). We
observe that the execution accuracy of queries constructed by our model on the
challenging Spider dataset is comparable with the state-of-the-art text-to-SQL
methods trained with annotated SQL queries. Our code and data are publicly
available (see https://github.com/yandex-research/sparqling-queries).
