Elisabeth Andre

Also published as: Elisabeth André


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“It’s our fault!”: Insights Into Users’ Understanding and Interaction With an Explanatory Collaborative Dialog System
Katharina Weitz | Lindsey Vanderlyn | Ngoc Thang Vu | Elisabeth André
Proceedings of the 25th Conference on Computational Natural Language Learning

Human-AI collaboration, a long standing goal in AI, refers to a partnership where a human and artificial intelligence work together towards a shared goal. Collaborative dialog allows human-AI teams to communicate and leverage strengths from both partners. To design collaborative dialog systems, it is important to understand what mental models users form about their AI-dialog partners, however, how users perceive these systems is not fully understood. In this study, we designed a novel, collaborative, communication-based puzzle game and explanatory dialog system. We created a public corpus from 117 conversations and post-surveys and used this to analyze what mental models users formed. Key takeaways include: Even when users were not engaged in the game, they perceived the AI-dialog partner as intelligent and likeable, implying they saw it as a partner separate from the game. This was further supported by users often overestimating the system’s abilities and projecting human-like attributes which led to miscommunications. We conclude that creating shared mental models between users and AI systems is important to achieving successful dialogs. We propose that our insights on mental models and miscommunication, the game, and our corpus provide useful tools for designing collaborative dialog systems.

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AVASAG: A German Sign Language Translation System for Public Services (short paper)
Fabrizio Nunnari | Judith Bauerdiek | Lucas Bernhard | Cristina España-Bonet | Corinna Jäger | Amelie Unger | Kristoffer Waldow | Sonja Wecker | Elisabeth André | Stephan Busemann | Christian Dold | Arnulph Fuhrmann | Patrick Gebhard | Yasser Hamidullah | Marcel Hauck | Yvonne Kossel | Martin Misiak | Dieter Wallach | Alexander Stricker
Proceedings of the 1st International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL)

This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services. We describe the scientific innovation points (geometry-based SL-description, 3D animation and video corpus, simplified annotation scheme, motion capture strategy) and the overall translation pipeline.


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Shaping a social robot’s humor with Natural Language Generation and socially-aware reinforcement learning
Hannes Ritschel | Elisabeth André
Proceedings of the Workshop on NLG for Human–Robot Interaction

Humor is an important aspect in human interaction to regulate conversations, increase interpersonal attraction and trust. For social robots, humor is one aspect to make interactions more natural, enjoyable, and to increase credibility and acceptance. In combination with appropriate non-verbal behavior, natural language generation offers the ability to create content on-the-fly. This work outlines the building-blocks for providing an individual, multimodal interaction experience by shaping the robot’s humor with the help of Natural Language Generation and Reinforcement Learning based on human social signals.


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Improving Automatic Emotion Recognition from Speech via Gender Differentiaion
Thurid Vogt | Elisabeth André
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Feature extraction is still a disputed issue for the recognition of emotions from speech. Differences in features for male and female speakers are a well-known problem and it is established that gender-dependent emotion recognizers perform better than gender-independent ones. We propose a way to improve the discriminative quality of gender-dependent features: The emotion recognition system is preceded by an automatic gender detection that decides upon which of two gender-dependent emotion classifiers is used to classify an utterance. This framework was tested on two different databases, one with emotional speech produced by actors and one with spontaneous emotional speech from a Wizard-of-Oz setting. Gender detection achieved an accuracy of about 90 % and the combined gender and emotion recognition system improved the overall recognition rate of a gender-independent emotion recognition system by 2-4 %.


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Planning Referential Acts for Animated Presentation Agents
Elisabeth Andre | Thomas Rist
Referring Phenomena in a Multimedia Context and their Computational Treatment


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Referring to World Objects With Text and Pictures
Elisabeth Andre | Thomas Rist
COLING 1994 Volume 1: The 15th International Conference on Computational Linguistics


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Designing Illustrated Texts: How Language Production Is Influenced by Graphics Generation
Wolfgang Wahlster | Elisabeth Andre | Winfried Graf | Thomas Rist
Fifth Conference of the European Chapter of the Association for Computational Linguistics