Anthony Hunter is a Professor of Artificial Intelligence in the Department of Computer Science, University College London. He is also Head of the Intelligent Systems Group, and Department Graduate Tutor. His research is in the area of machine reasoning which is a branch of artificial intelligence. Reasoning is a critically important faculty of human intelligence, and machine reasoning is concerned with capturing aspects of this for use in software.

The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). Recent developments in computational modelling of argument (a subfield of AI) are leading to technology for persuasion that can potentially be harnessed in behaviour change applications. Using this technology, a software system and a user can exchange arguments in a dialogue. So the system gains information about the user’s perspective, provides arguments to fill gaps in the user’s knowledge, and attempts to overturn misconceptions held by the user. Our work has focused on modelling the beliefs and concerns of the user, and harnessing these to make the best choices of move during the dialogue for persuading the user to change their behaviour. During this talk, the background to, and components of, our approach will be presented together with some promising preliminary results with participants.

Lu Wang is an Assistant Professor of Computer Science and Engineering at University of Michigan. Lu's research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects.

Understanding, evaluating, and generating arguments are crucial elements of the decision-making and reasoning process. A multitude of arguments and counter-arguments are constructed on a daily basis to persuade and inform us on a wide range of issues. However, constructing persuasive arguments is a challenging task for both human and computers, as it requires credible evidence, rigorous logical reasoning, and sometimes emotional appeals.
In this talk, I will introduce our neural network-based argument generation framework. Our two-step argument generation model separately tackles the challenges of content planning and surface realization, and can be optionally augmented with a powerful retrieval system. I then discuss how to extend the model to conduct dynamic content planning with mixed language models. We believe that the proposed argument generation framework will enable many compelling applications, including providing unbiased perspectives on complex issues, debate coaching, and essay writing tutoring. Our framework is also generic and has been applied to other text generation problems, e.g., writing news and Wikipedia articles.

Anette is the chair of Computational Linguistics at Heidelberg University. Her research focuses on discourse-oriented semantics using neural methods for advanced NLU tasks, such as reading comprehension, commonsense reasoning or argumentation analysis. Anette is especially interested in combining neural machine learning methods with linguistic modeling and knowledge representation, which she believes will make NLP systems more powerful and robust, and deeper in modeling Natural Language Understanding.

Ivan is the head of the Trustworthy Human Language Technologies group at TU Darmstadt. His current research areas include legal argument mining, privacy-preserving NLP, and explainable and trustworthy models. Ivan's research track spans argument mining and computational argumentation, crowdsourcing, large-scale corpora, serious games, sentiment and sarcasm on social media, and semantic web.

Gabriella leads the independent research group E-DELIB at the University of Stuttgart. Broadly speaking, Gabriella is a computational linguist with a strong interest in the applications of NLP in interdisciplinary settings. Her domains of interest are Political Science (i.e., supporting decision-making in forums with NLP; capturing the dynamics of political debates based on newspaper reports), cognitive modeling (i.e., prediction of priming or and free associations), and theoretical linguistics (i.e., semantics of morphological derivation).

Henning is the chair of the Computational Social Science group at Paderborn University. Among the main research topics of his group are the computational analysis and synthesis of argumentation, the detection and mitigation of social bias and media bias in text, and the construction of human-like explanations in educational and explainable artificial intelligence.