Papers by Ionel Alexandru Hosu

2 papers
Natural Language Interface for Databases Using a Dual-Encoder Model (C18-1)

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Challenge: Existing approaches to train data-driven natural language interfaces for databases are limited and lack of large datasets is probably the main reason for the lack of complex machine learning approaches.
Approach: They propose a sketch-based two-step neural model for generating structured queries based on a user’s request in natural language.
Outcome: The proposed model improves on two recent large datasets suitable for data-driven solutions for natural language interfaces for databases.
Neural Approaches for Natural Language Interfaces to Databases: A Survey (2020.coling-main)

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Challenge: Interest in NLIDBs has resurged in the past years due to the availability of large datasets and improvements to neural sequence-to-sequence models.
Approach: They focus on key design decisions behind current state of the art neural approaches . they highlight linking question tokens to database schema elements .
Outcome: The proposed approaches are grouped into encoder and decoder improvements . they include better architectures for encoding the textual query taking into account the schema and improved generation of structured queries using autoregressive neural models.

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