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Found 4 papers, 2 papers with code
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation
Text-to-SQL semantic parsing has made significant progress in recent years, with various models demonstrating impressive performance on the challenging Spider benchmark.
Searching for Better Database Queries in the Outputs of Semantic Parsers
The task of generating a database query from a question in natural language suffers from ambiguity and insufficiently precise description of the goal.
SPARQLing Database Queries from Intermediate Question Decompositions
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).
Cost-Sensitive Training for Autoregressive Models
Training autoregressive models to better predict under the test metric, instead of maximizing the likelihood, has been reported to be beneficial in several use cases but brings additional complications, which prevent wider adoption.
