  Collecting and annotating task-oriented dialog data is difficult, especially
for highly specific domains that require expert knowledge. At the same time,
informal communication channels such as instant messengers are increasingly
being used at work. This has led to a lot of work-relevant information that is
disseminated through those channels and needs to be post-processed manually by
the employees. To alleviate this problem, we present TexPrax, a messaging
system to collect and annotate problems, causes, and solutions that occur in
work-related chats. TexPrax uses a chatbot to directly engage the employees to
provide lightweight annotations on their conversation and ease their
documentation work. To comply with data privacy and security regulations, we
use an end-to-end message encryption and give our users full control over their
data which has various advantages over conventional annotation tools. We
evaluate TexPrax in a user-study with German factory employees who ask their
colleagues for solutions on problems that arise during their daily work.
Overall, we collect 202 task-oriented German dialogues containing 1,027
sentences with sentence-level expert annotations. Our data analysis also
reveals that real-world conversations frequently contain instances with
code-switching, varying abbreviations for the same entity, and dialects which
NLP systems should be able to handle.
