Preface from the Program Chair

It is well-known that natural language has built-in redundancy. By using context, we can often guess the next word or character in a text. Two practical communities have independently exploited this fact. First, automatic speech and translation researchers build language models to distinguish fluent from non-fluent outputs. Second, text compression researchers convert predictions into short encodings, to save disk space and bandwidth. I will explore what these two communities can learn from each others’ (interestingly different) solutions. Then I will look at the less-studied question of redundancy in bilingual text, addressing questions like "How well can we predict human translator behavior?" and "How much information does a human translator add to the original?" (This is joint work with Barret Zoph and Marjan Ghazvininejad.) Bio Kevin Knight is Director of Natural Language Technologies at the Information Sciences Institute (ISI) of the University of Southern California (USC), and a Professor in the USC Computer Science Department. He received a PhD in computer science from Carnegie Mellon University and a bachelor’s degree from Harvard University. Prof. Knight’s research interests include machine translation, automata theory, and decipherment of historical manuscripts. Prof. Knight co-wrote the textbook "Artificial Intelligence", served as President of the Association for Computational Linguistics, and was a co-founder of the machine translation company Language Weaver, Inc. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Association for Computational Linguistics (ACL), and the Information Sciences Institute (ISI). Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015) xi Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015) xii INVITED TALK: Modeling Socio-Emotional Humanoid Agent Catherine Pelachaud CNRS-LTCI, TELECOM-ParisTech, France catherine.pelachaud@telecom-paristech.fr Abstract In this talk, I will present our current work toward endowing virtual agents with socio-emotional capabilities. I will start describing an interactive system of an agent dialoging with human users in an emotionally colored manner. Through its behaviors, the agent can sustain a conversation as well as show various attitudes and levels of engagement. I will present our latest work on laughter. I will address several issues such as: how to animate laughter in a virtual agent looking particularly at rhythmic movements; how to laugh with human participant and how laughing agent is perceived.In this talk, I will present our current work toward endowing virtual agents with socio-emotional capabilities. I will start describing an interactive system of an agent dialoging with human users in an emotionally colored manner. Through its behaviors, the agent can sustain a conversation as well as show various attitudes and levels of engagement. I will present our latest work on laughter. I will address several issues such as: how to animate laughter in a virtual agent looking particularly at rhythmic movements; how to laugh with human participant and how laughing agent is perceived.