Fuat Basik
ABSTRACT
In this demo, we present DBPal, a novel data exploration tool with a natural language interface. 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.
- I. Androutsopoulos et al. Natural language interfaces to databases - an introduction. Natural Language Engineering, 1:29--81, 1995.Google Scholar
Cross Ref
- D. Bahdanau et al. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473, 2014.Google Scholar
- A. Crotty et al. Vizdom: Interactive analytics through pen and touch. PVLDB, 8(12):2024--2035, 2015. Google ScholarDigital Library
- S. Iyer et al. Learning a neural semantic parser from user feedback. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pages 963--973, 2017.Google ScholarCross Ref
- F. Li et al. Constructing an interactive natural language interface for relational databases. PVLDB, 8:73--84, 2014. Google ScholarDigital Library
- E. Pavlick et al. Simple PPDB: A paraphrase database for simplification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7--12, 2016, Berlin, Germany, Volume 2: Short Papers, 2016.Google Scholar
- A.-M. Popescu et al. Towards a theory of natural language interfaces to databases. In IUI, 2003. Google ScholarDigital Library
- D. Saha and othersa. Athena: An ontology-driven system for natural language querying over relational data stores. Proc. VLDB Endow., 9(12):1209--1220, Aug. 2016. Google ScholarDigital Library
- I. Sutskever et al. Sequence to sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems, NIPS'14, pages 3104--3112, Cambridge, MA, USA, 2014. MIT Press. Google ScholarDigital Library
- P. Terlecki et al. On improving user response times in tableau. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31 - June 4, 2015, SIGMOD '15, pages 1695--1706, 2015. Google ScholarDigital Library
- M. Vila et al. Paraphrase concept and typology. A linguistically based and computationally oriented approach. Procesamiento del Lenguaje Natural, 46:83--90, 2011.Google Scholar
- Y. Wang et al. Building a semantic parser overnight. In ACL, 2015.Google ScholarCross Ref
Index Terms
- DBPal: A Learned NL-Interface for Databases
Recommendations
DBPal: A Fully Pluggable NL2SQL Training Pipeline
SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of DataNatural language is a promising alternative interface to DBMSs because it enables non-technical users to formulate complex questions in a more concise manner than SQL. Recently, deep learning has gained traction for translating natural language to SQL, ...
A proposed architecture for dynamically built NLIDB systems
Natural Language Interface to Database (NLIDB) systems have provided an easy access to database system without the need for the user to use formal query languages, such as SQL. Database query languages can be difficult to the non-expert users and ...
Database Skeleton and Its Application to Fuzzy Query Translation
This paper treats the problem of fuzzy query translation using the concept of database skeleton. An imprecise, or fuzzy, query is represented by a query graph, which can be analyzed for translatability with respect to a given database skeleton. The ...
Comments