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In this paper, we introduce a novel methodology to efficiently construct acorpus for question answering over structured data. For this, we introduce anintermediate representation that is based on the logical query plan in adatabase called Operation Trees (OT). This representation allows us to invertthe annotation process without losing flexibility in the types of queries thatwe generate. Furthermore, it allows for fine-grained alignment of query tokensto OT operations. In our method, we randomly generate OTs from a context-freegrammar. Afterwards, annotators have to write the appropriate natural languagequestion that is represented by the OT. Finally, the annotators assign thetokens to the OT operations. We apply the method to create a new corpus OTTA(Operation Trees and Token Assignment), a large semantic parsing corpus forevaluating natural language interfaces to databases. We compare OTTA to Spiderand LC-QuaD 2.0 and show that our methodology more than triples the annotationspeed while maintaining the complexity of the queries. Finally, we train astate-of-the-art semantic parsing model on our data and show that our corpus isa challenging dataset and that the token alignment can be leveraged to increasethe performance significantly.

A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation