Gianna Weber


2021

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“It seemed like an annoying woman”: On the Perception and Ethical Considerations of Affective Language in Text-Based Conversational Agents
Lindsey Vanderlyn | Gianna Weber | Michael Neumann | Dirk Väth | Sarina Meyer | Ngoc Thang Vu
Proceedings of the 25th Conference on Computational Natural Language Learning

Previous research has found that task-oriented conversational agents are perceived more positively by users when they provide information in an empathetic manner compared to a plain, emotionless information exchange. However, users’ perception and ethical considerations related to a dialog systems’ response language style have received comparatively little attention in the field of human-computer interaction. To bridge this gap, we explored these ethical implications through a scenario-based user study. 127 participants interacted with one of three variants of an affective, task-oriented conversational agent, each variant providing responses in a different language style. After the interaction, participants filled out a survey about their feelings during the experiment and their perception of various aspects of the chatbot. Based on statistical and qualitative analysis of the responses, we found language style played an important role in how human-like participants perceived a dialog agent as well as how likable. Language style also had a direct effect on how users perceived the use of personal pronouns ‘I’ and ‘You’ and how they projected gender onto the chatbot. Finally, we identify and discuss ethical implications. In particular we focus on what factors/stereotypes influenced participants’ impressions of gender, and what trade-offs a more human-like chatbot brings.

2019

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ADVISER: A Dialog System Framework for Education & Research
Daniel Ortega | Dirk Väth | Gianna Weber | Lindsey Vanderlyn | Maximilian Schmidt | Moritz Völkel | Zorica Karacevic | Ngoc Thang Vu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

In this paper, we present ADVISER - an open source dialog system framework for education and research purposes. This system supports multi-domain task-oriented conversations in two languages. It additionally provides a flexible architecture in which modules can be arbitrarily combined or exchanged - allowing for easy switching between rules-based and neural network based implementations. Furthermore, ADVISER offers a transparent, user-friendly framework designed for interdisciplinary collaboration: from a flexible back end, allowing easy integration of new features, to an intuitive graphical user interface supporting nontechnical users.