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Investigating the effect of Mental Models in User Interaction with an Adaptive Dialog Agent
Lindsey Vanderlyn, Dirk Väth, Ngoc Thang Vu
Mental models play an important role in whether user interaction with intelligent systems, such as dialog systems is successful or not. Adaptive dialog systems present the opportun…
Towards a Zero-Data, Controllable, Adaptive Dialog System
Dirk Väth, Lindsey Vanderlyn, Ngoc Thang Vu
Conversational Tree Search (Väth et al., 2023) is a recent approach to controllable dialog systems, where domain experts shape the behavior of a Reinforcement Learning agent throug…
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings
Wei Zhou, Heike Adel, Hendrik Schuff +1
Attribution scores indicate the importance of different input parts and can, thus, explain model behaviour. Currently, prompt-based models are gaining popularity, i.a., due to thei…
Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers
Manuel Mager, Elisabeth Mager, Katharina Kann +1
In recent years machine translation has become very successful for high-resource language pairs. This has also sparked new interest in research on the automatic translation of low-…
Neighboring Words Affect Human Interpretation of Saliency Explanations
Alon Jacovi, Hendrik Schuff, Heike Adel +2
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such…
Conversational Tree Search: A New Hybrid Dialog Task
Dirk Väth, Lindsey Vanderlyn, Ngoc Thang Vu
Conversational interfaces provide a flexible and easy way for users to seek information that may otherwise be difficult or inconvenient to obtain. However, existing interfaces gene…