{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T17:53:15Z","timestamp":1760205195230},"reference-count":4,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>\n            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\n            <jats:italic>CAT<\/jats:italic>\n            , which can be used to easily create conversational agents for transactional databases. The main idea is that, for a given OLTP database,\n            <jats:italic>CAT<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.14778\/3554821.3554850","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3586-3589","source":"Crossref","is-referenced-by-count":1,"title":["Demonstrating CAT"],"prefix":"10.14778","volume":"15","author":[{"given":"Marius","family":"Gassen","sequence":"first","affiliation":[{"name":"Technical University of Darmstadt"}]},{"given":"Benjamin","family":"H\u00e4ttasch","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt"}]},{"given":"Benjamin","family":"Hilprecht","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt"}]},{"given":"Nadja","family":"Geisler","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt"}]},{"given":"Alexander","family":"Fraser","sequence":"additional","affiliation":[{"name":"LMU Munich"}]},{"given":"Carsten","family":"Binnig","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3115\/116580.116613"},{"key":"e_1_2_1_2_1","volume-title":"Heck","author":"Shah Pararth","year":"2018","unstructured":"Pararth Shah , Dilek Hakkani-T\u00fcr , G\u00f6khan T\u00fcr , Abhinav Rastogi , Ankur Bapna , Neha Nayak , and Larry P . Heck . 2018 . Building a Conversational Agent Overnight with Dialogue Self-Play. CoRR abs\/1801.04871 (2018). arXiv:1801.04871 http:\/\/arxiv.org\/abs\/1801.04871 Pararth Shah, Dilek Hakkani-T\u00fcr, G\u00f6khan T\u00fcr, Abhinav Rastogi, Ankur Bapna, Neha Nayak, and Larry P. Heck. 2018. Building a Conversational Agent Overnight with Dialogue Self-Play. CoRR abs\/1801.04871 (2018). arXiv:1801.04871 http:\/\/arxiv.org\/abs\/1801.04871"},{"key":"e_1_2_1_3_1","unstructured":"Nathaniel Weir Andrew Crotty Alex Galakatos Amir Ilkhechi Shekar Ramaswamy Rohin Bhushan Ugur Cetintemel Prasetya Utama Nadja Geisler Benjamin H\u00e4ttasch Steffen Eger and Carsten Binnig. 2019. DBPal: Weak Supervision for Learning a Natural Language Interface to Databases. arXiv:1909.06182 [cs.DB]  Nathaniel Weir Andrew Crotty Alex Galakatos Amir Ilkhechi Shekar Ramaswamy Rohin Bhushan Ugur Cetintemel Prasetya Utama Nadja Geisler Benjamin H\u00e4ttasch Steffen Eger and Carsten Binnig. 2019. DBPal: Weak Supervision for Learning a Natural Language Interface to Databases. arXiv:1909.06182 [cs.DB]"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-3601"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554850","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:30:28Z","timestamp":1672227028000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554850"}},"subtitle":["synthesizing data-aware conversational agents for transactional databases"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":4,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554850"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554850","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}