Explanatory dialogue systems serve as intuitive interface between non-expert users and explainable AI (XAI) systems. The interaction with these kind of systems benefits especially from the integration of structured domain knowledge, e.g., by means of bipolar argumentation trees. So far, these domain-specific structures need to be created manually, therewith impairing the flexibility of the system with respect to the domain. We address this limitation by adapting an existing pipeline for topic-independent acquisition of argumentation trees in the field of persuasive, argumentative dialogue to the area of explanatory dialogue. This shift is achieved by a) introducing and investigating different formulations of auxiliary claims per feature of the explanation of the AI model, b) exploring the influence of pre-grouping of the arguments with respect to the feature they address, c) suggesting adaptions to the existing algorithm of the pipeline for obtaining a tree structure, and d) utilizing a new approach for determining the type of the relationship between the arguments. Through a step-wise expert evaluation for the domain titanic survival, we identify the best performing variant of our pipeline. With this variant we conduct a user study comparing the automatically generated argumentation trees against their manually created counterpart in the domains titanic survival and credit acquisition. This assessment of the suitability of the generated argumentation trees for a later integration into dialogue-based XAI systems as domain knowledge yields promising results.

Explainable artificial intelligence (XAI) is a rapidly evolving field that seeks to create AI systems that can provide human-understandable explanations for their decision-making processes. However, these explanations rely on model and data-specific information only. To support better human decision-making, integrating domain knowledge into AI systems is expected to enhance understanding and transparency. In this paper, we present an approach for combining XAI explanations with domain knowledge within a dialogue system. We concentrate on techniques derived from the field of computational argumentation to incorporate domain knowledge and corresponding explanations into human-machine dialogue. We implement the approach in a prototype system for an initial user evaluation, where users interacted with the dialogue system to receive predictions from an underlying AI model. The participants were able to explore different types of explanations and domain knowledge. Our results indicate that users tend to more effectively evaluate model performance when domain knowledge is integrated. On the other hand, we found that domain knowledge was not frequently requested by the user during dialogue interactions.

Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations. Since the manual acquisition of these argument structures is highly time-consuming, the corresponding systems are inflexible regarding the topics they can discuss. To address this issue, we propose a combination of argumentative dialogue systems with argument search technology that enables a system to discuss any topic on which the search engine is able to find suitable arguments. Our approach utilizes supervised learning-based relation classification to map the retrieved arguments into a general tree structure for use in dialogue systems. We evaluate the approach with a state of the art search engine and a recently introduced dialogue model in an extensive user study with respect to the dialogue coherence. The results vary between the investigated topics (and hence depend on the quality of the underlying data) but are in some instances surprisingly close to the results achieved with a manually annotated argument structure.

We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments. To this end, we introduce a dialogue system that presents arguments by means of a virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories (Interesting, Convincing, Comprehensible, Relation). The approach is applied in a user study in order to compare two state of the art argument search engines to each other and with a system based on traditional web search. The results show a significant advantage of the two search engines over the baseline. Moreover, the two search engines show significant advantages over each other in different categories, thereby reflecting strengths and weaknesses of the different underlying techniques.

For estimating the Interaction Quality (IQ) in Spoken Dialogue Systems (SDS), the dialogue history is of significant importance. Previous works included this information manually in the form of precomputed temporal features into the classification process. Here, we employ a deep learning architecture based on Long Short-Term Memories (LSTM) to extract this information automatically from the data, thus estimating IQ solely by using current exchange features. We show that it is thereby possible to achieve competitive results as in a scenario where manually optimized temporal features have been included.