Gassen, Marius ; Hättasch, Benjamin ; Hilprecht, Benjamin ; Geisler, Nadja ; Fraser, Alexander ; Binnig, Carsten (2022)
Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases.
In: Proceedings of the VLDB Endowment, 15 (12)
doi: 10.14778/3554821.3554850
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Databases for OLTP are often the backbone for applications such as hotel room or cinema ticket booking applications. However, developing a conversational agent (i.e., a chatbot-like interface) to allow end-users to interact with an application using natural language requires both immense amounts of training data and NLP expertise. This motivates CAT, which can be used to easily create conversational agents for transactional databases. The main idea is that, for a given OLTP database, CAT uses weak supervision to synthesize the required training data to train a state-of-the-art conversational agent, allowing users to interact with the OLTP database. Furthermore, CAT provides an out-of-the-box integration of the resulting agent with the database. As a major difference to existing conversational agents, agents synthesized by CAT are data-aware. This means that the agent decides which information should be requested from the user based on the current data distributions in the database, which typically results in markedly more efficient dialogues compared with non-data-aware agents. We publish the code for CAT as open source.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Gassen, Marius ; Hättasch, Benjamin ; Hilprecht, Benjamin ; Geisler, Nadja ; Fraser, Alexander ; Binnig, Carsten |
| Art des Eintrags: | Bibliographie |
| Titel: | Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for Transactional Databases |
| Sprache: | Englisch |
| Publikationsjahr: | 5 September 2022 |
| Verlag: | ACM |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Proceedings of the VLDB Endowment |
| Jahrgang/Volume einer Zeitschrift: | 15 |
| (Heft-)Nummer: | 12 |
| DOI: | 10.14778/3554821.3554850 |
| URL / URN: | https://dl.acm.org/doi/10.14778/3554821.3554850 |
| Kurzbeschreibung (Abstract): | Databases for OLTP are often the backbone for applications such as hotel room or cinema ticket booking applications. However, developing a conversational agent (i.e., a chatbot-like interface) to allow end-users to interact with an application using natural language requires both immense amounts of training data and NLP expertise. This motivates CAT, which can be used to easily create conversational agents for transactional databases. The main idea is that, for a given OLTP database, CAT uses weak supervision to synthesize the required training data to train a state-of-the-art conversational agent, allowing users to interact with the OLTP database. Furthermore, CAT provides an out-of-the-box integration of the resulting agent with the database. As a major difference to existing conversational agents, agents synthesized by CAT are data-aware. This means that the agent decides which information should be requested from the user based on the current data distributions in the database, which typically results in markedly more efficient dialogues compared with non-data-aware agents. We publish the code for CAT as open source. |
| Freie Schlagworte: | systems_cat |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data and AI Systems |
| Hinterlegungsdatum: | 06 Jun 2023 12:42 |
| Letzte Änderung: | 02 Aug 2023 13:47 |
| PPN: | 510089097 |
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| Suche nach Titel in: | TUfind oder in Google |
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