Neural Natural Language Database Interface System

A Neural Natural Language Database Interface System is a Natural Language Database Interface System that is based on an Artificial Neural Network Model.

• AKA: Neural Natural Language Interface to Databases System.
• Example(s):
  • DBPal.
  • …
• Counter-Example(s):
  • AlLaDIn,
  • NaLIX,
  • NaLIR.
• See: Neural Query Translation System, Neural Semantic Parser, Neural Translation System, User Interface, Natural Language Processing, Natural Language Understanding, Natural Language Generation, Question Answering Task.

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References

2018a

• (Utama et al., 2018) ⇒ Prasetya Utama, Nathaniel Weir, Fuat Basik, Carsten Binnig, Ugur Cetintemel, Benjamin Hattasch, Amir Ilkhechi, Shekar Ramaswamy, and Arif Usta. (2018). “An End-to-end Neural Natural Language Interface for Databases.” In: CoRR Journal, abs/1804.00401. arXiv:1804.00401
  • QUOTE: The current prototype of DBPal already shows a significant improvement over other state-of-the-art-systems such as NaLIR when dealing with paraphrasing and other linguistic variations. Furthermore, compared to other recent NLIDB approaches that leverage deep models for the query translation from NL to SQL, DBPal only requires minimal manual effort.

2018b

• (Basik et al., 2018) ⇒ Fuat Basik, Benjamin Hättasch, Amir Ilkhechi, Arif Usta, Shekar Ramaswamy, Prasetya Utama, Nathaniel Weir, Carsten Binnig, and Ugur Cetintemel. (2018). “DBPal: A Learned NL-Interface for Databases.” In: Proceedings of the 2018 International Conference on Management of Data (SIGMOD 2018). ISBN:978-1-4503-4703-7 doi:10.1145/3183713.3193562
  • QUOTE: ... DBPal leverages recent advances in deep models to make query understanding more robust in the following ways: First, DBPal uses novel machine translation models to translate natural language statements to SQL, making the translation process more robust to paraphrasing and linguistic variations. Second, to support the users in phrasing questions without knowing the database schema and the query features, DBPal provides a learned auto-completion model that suggests to users partial query extensions during query formulation and thus helps to write complex queries.

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