Weir, Nathaniel ; Crotty, Andrew ; Galakatos, Alex ; Ilkhechi, Amir ; Ramaswamy, Shekar ; Bhushan, Rohin ; Cetintemel, Ugur ; Utama, Prasetya ; Geisler, Nadja ; Hättasch, Benjamin ; Eger, Steffen ; Binnig, Carsten (2019)
DBPal: Weak Supervision for Learning a Natural Language Interface to Databases.
1st International Workshop on Conversational Access to Data (CAST) in conj. with the 45th International Conference on Very Large Data Bases (VLDB). Los Angeles, California, USA
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
This paper describes DBPal, a new system to translate natural language utterances into SQL statements using a neural machine translation model. While other recent approaches use neural machine translation to implement a Natural Language Interface to Databases (NLIDB), existing techniques rely on supervised learning with manually curated training data, which results in substantial overhead for supporting each new database schema. In order to avoid this issue, DBPal implements a novel training pipeline based on weak supervision that synthesizes all training data from a given database schema. In our evaluation, we show that DBPal can outperform existing rule-based NLIDBs while achieving comparable performance to other NLIDBs that leverage deep neural network models without relying on manually curated training data for every new database schema.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Weir, Nathaniel ; Crotty, Andrew ; Galakatos, Alex ; Ilkhechi, Amir ; Ramaswamy, Shekar ; Bhushan, Rohin ; Cetintemel, Ugur ; Utama, Prasetya ; Geisler, Nadja ; Hättasch, Benjamin ; Eger, Steffen ; Binnig, Carsten |
| Art des Eintrags: | Bibliographie |
| Titel: | DBPal: Weak Supervision for Learning a Natural Language Interface to Databases |
| Sprache: | Englisch |
| Publikationsjahr: | 30 August 2019 |
| Ort: | Los Angeles, California, USA |
| Veranstaltungstitel: | 1st International Workshop on Conversational Access to Data (CAST) in conj. with the 45th International Conference on Very Large Data Bases (VLDB) |
| Veranstaltungsort: | Los Angeles, California, USA |
| Kurzbeschreibung (Abstract): | This paper describes DBPal, a new system to translate natural language utterances into SQL statements using a neural machine translation model. While other recent approaches use neural machine translation to implement a Natural Language Interface to Databases (NLIDB), existing techniques rely on supervised learning with manually curated training data, which results in substantial overhead for supporting each new database schema. In order to avoid this issue, DBPal implements a novel training pipeline based on weak supervision that synthesizes all training data from a given database schema. In our evaluation, we show that DBPal can outperform existing rule-based NLIDBs while achieving comparable performance to other NLIDBs that leverage deep neural network models without relying on manually curated training data for every new database schema. |
| Freie Schlagworte: | dm, dm_nlidb, dm_dbpal |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data Management (2022 umbenannt in Data and AI Systems) DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 24 Jul 2019 13:17 |
| Letzte Änderung: | 21 Apr 2020 15:08 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum