Human users tend to selectively ignore information that contradicts their pre-existing beliefs or opinions in their process of information seeking. These “self-imposed filter bubbles” (SFB) pose a significant challenge for cooperative argumentative dialogue systems aiming to build an unbiased opinion and a better understanding of the topic at hand. To address this issue, we develop a strategy for overcoming users’ SFB within the course of the interaction. By continuously modeling the user’s position in relation to the SFB, we are able to identify the respective arguments which maximize the probability to get outside the SFB and present them to the user. We implemented this approach in an argumentative dialogue system and evaluated in a laboratory user study with 60 participants to show its validity and applicability. The findings suggest that the strategy was successful in breaking users’ SFBs and promoting a more reflective and comprehensive discussion of the topic.

Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest. The scarce existing research on user interest in dialogue systems depends on explicit user feedback. Such systems require user responses that are not content-related and thus, tend to disturb the dialogue flow. In this paper, we present a novel model for implicitly estimating user interest during argumentative dialogues based on semantically clustered data. Therefore, an online user study was conducted to acquire training data which was used to train a binary neural network classifier in order to predict whether or not users are still interested in the content of the ongoing dialogue. We achieved a classification accuracy of 74.9% and furthermore investigated with different Artificial Neural Networks (ANN) which new argument would fit the user interest best.

Speech interfaces for argumentative dialogue systems (ADS) are rather scarce. The complex task they pursue hinders the application of common natural language understanding (NLU) approaches in this domain. To address this issue we include an adaption of a recently introduced NLU framework tailored to argumentative tasks into a complete ADS. We evaluate the likeability and motivation of users to interact with the new system in a user study. Therefore, we compare it to a solid baseline utilizing a drop-down menu. The results indicate that the integration of a flexible NLU framework enables a far more natural and satisfying interaction with human users in real-time. Even though the drop-down menu convinces regarding its robustness, the willingness to use the new system is significantly higher. Hence, the featured NLU framework provides a sound basis to build an intuitive interface which can be extended to adapt its behavior to the individual user.

The growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker’s sentiment from speech only. Mapping the latter on user preferences enables to adapt to the user and individualize the requested information while increasing user satisfaction. In this paper, we explore the integration of rank consistent ordinal regression into a speech-only sentiment prediction task performed by ResNet-like systems. Furthermore, we use speaker verification extractors trained on larger datasets as low-level feature extractors. An improvement of performance is shown by fusing sentiment and pre-extracted speaker embeddings reducing the speaker bias of sentiment predictions. Numerous experiments on Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) databases show that we beat the baselines of state-of-the-art unimodal approaches. Using speech as the only modality combined with optimizing an order-sensitive objective function gets significantly closer to the sentiment analysis results of state-of-the-art multimodal systems.

To build a well-founded opinion it is natural for humans to gather and exchange new arguments. Especially when being confronted with an overwhelming amount of information, people tend to focus on only the part of the available information that fits into their current beliefs or convenient opinions. To overcome this “self-imposed filter bubble” (SFB) in the information seeking process, it is crucial to identify influential indicators for the former. Within this paper we propose and investigate indicators for the the user’s SFB, mainly their Reflective User Engagement (RUE), their Personal Relevance (PR) ranking of content-related subtopics as well as their False (FK) and True Knowledge (TK) on the topic. Therefore, we analysed the answers of 202 participants of an online conducted user study, who interacted with our argumentative dialogue system BEA (“Building Engaging Argumentation”). Moreover, also the influence of different input/output modalities (speech/speech and drop-down menu/text) on the interaction with regard to the suggested indicators was investigated.