@inproceedings{stangier-etal-2022-texprax,
    title = "{T}ex{P}rax: A Messaging Application for Ethical, Real-time Data Collection and Annotation",
    author = {Stangier, Lorenz  and
      Lee, Ji-Ung  and
      Wang, Yuxi  and
      M{\"u}ller, Marvin  and
      Frick, Nicholas  and
      Metternich, Joachim  and
      Gurevych, Iryna},
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2022",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.aacl-demo.2",
    pages = "9--16",
    abstract = "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.",
}