@InProceedings{Michael2017_246,
<br/>   author = {Thilo Michael and Stefan Hillmann and Benjamin Weiss},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2017},
<br/>   title = {Alex: An Artificial Conversational Agent for Students at the TU Berlin},
<br/>   year = {2017},
<br/>   editor = {Jürgen Trouvain and Ingmar Steiner and Bernd Möbius},
<br/>   month = mar,
<br/>   pages = {238--245},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {User interfaces of most data-centered software systems are oriented on
the data structure of the respective application. While such interfaces work well for
trained users, they are most often not suitable for non-expert users. In this paper,
we present ALEX, an advisory artificial conversational agent that uses machine
learning and natural language processing to gather information from a relational
database. Specifically, ALEX allows students at the Technische Universität Berlin
(TU Berlin) to query courses and modules in a conversational way using textual
input. The natural language understanding of the system is realized with a pipeline
that uses a Hidden Markov Model tagger to annotate the input and groups phrases
together to form SQL queries. ALEX was evaluated in a research study that compared
the conversational agent with the existing online systems of the TU Berlin
which are currently used by students to retrieve information about courses. The
results show that the conversational approach is not only more efficient with nonexpert
users, but also has higher hedonic and pragmatic quality. A live demo of the
tool can be found at http://alex.qu.tu-berlin.de.},
<br/>   isbn = {978-3-959080-92-7},
<br/>   issn = {0940-6832},
<br/>   keywords = {Poster},
<br/>   url = {https://www.essv.de/pdf/2017_238_245.pdf},
<br/>}