NLIQ frames natural language querying around target adequacy to classify when the output representation must be constructed, treating intermediate representations as first-class semantic objects rather than mere implementation steps.
Proceedings of the VLDB Endowment8(1), 73–84 (2014)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Relation-aware self-attention encodes schema structure for text-to-SQL, raising exact-match accuracy on Spider from 18.96% to 42.94%.
Introduces Packed Plan Forest (PPF) as a polynomially bounded structure that encodes feasible ambiguous logical plans while pruning infeasible ones in cross-model NL-to-DB query planning.
citing papers explorer
-
Natural Language to What? A Vision for Intermediate Representations in NL-to-X Querying
NLIQ frames natural language querying around target adequacy to classify when the output representation must be constructed, treating intermediate representations as first-class semantic objects rather than mere implementation steps.
-
Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL Parsers
Relation-aware self-attention encodes schema structure for text-to-SQL, raising exact-match accuracy on Spider from 18.96% to 42.94%.
-
Feasible Plan Generation with Ambiguity-Boundedness in Cross-Model Query Processing
Introduces Packed Plan Forest (PPF) as a polynomially bounded structure that encodes feasible ambiguous logical plans while pruning infeasible ones in cross-model NL-to-DB query planning.