TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation

TL;DR

TexPrax is a secure messaging system designed to facilitate real-time collection and annotation of work-related conversations, helping to gather task-oriented data with expert annotations while respecting privacy regulations.

Contribution

The paper introduces TexPrax, a novel messaging platform with integrated annotation features that ensures data privacy and is evaluated in a real-world industrial setting.

Findings

Collected 202 German task-oriented dialogues with expert annotations

Revealed challenges like code-switching and dialects in real-world conversations

Demonstrated the system's effectiveness in a factory environment

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.