  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.
