This research revolves around measuring the quality of arguments. High-quality arguments help in improving political discussions, resulting in better decision-making. Wachsmuth et al. developed a taxonomy breaking down argument quality into several dimensions. This work makes use of that taxonomy and combines it with modern NLP models. A cross-dataset examination of argument quality models was conducted. In particular, models were investigated on their generalizability between dimensions. Overall results show that there is no large difference in accuracy and agreement when models predict data of a quality dimension they were trained on, over dimensions they were not trained on. One can conclude that generalizations of argument quality dimensions with language models were not found. Nevertheless, qualitative analysis highlights findings that indicate some generalization to other dimensions.

Moral values influence humans in decision-making. Pluralist moral philosophers argue that human morality can be represented by a finite number of moral values, respecting the differences in moral views. Recent advancements in NLP show that language models retain a discernible level of knowledge in deontological ethics and moral norms of society. However, a model which can only decide either right or wrong cannot fully understand the diverse moral perspectives of humans.

We propose a moral sentence embedding space, which can encompass moral differences, through the state-of-the-art Contrastive Learning framework. We evaluate the moral embedding space both intrinsically and extrinsically via three tasks: classification, moral similarity, and visual analysis. We show that our moral embedding space understands the characteristics of each moral value. Our results also highlight that moral rhetoric is seldom explicit in the text, emphasizing the necessity of additional information such as moral labels.

This paper presents a novel approach to detect agreement and disagreement moments between participants in meeting transcripts without relying on labeled data. We propose a model in which disagreement detection is defined as the process of first identifying argumentative theses relevant to a given corpus of text and then classifying all phrases in the text as being either in favor of, against or expressing no opinion on a given thesis. To identify relevant theses, we compare the performance of a latent Dirichlet allocation-based topic model against that of a diverse set of large language models. To classify the stance of a phrase with respect to a thesis, only large language models are used. We find that, while state-of-the-art large language models do not outperform topic modeling-based approaches in extracting semantically relevant content, they are capable of presenting such content in a more concise and grammatically correct manner. We also find that state-of-the-art large language models are not capable of accurately performing stance classification as described above.

In April 2020, a Dutch research team swiftly analyzed public opinions on COVID-19 lockdown relaxations. However, due to time constraints, only a small amount of opinion data could be processed. With the surge of popularity in the field of Natural Language Processing (NLP) and the arrival of tools like ChatGPT, a number of tasks involving Large Language Models (LLMs) have become easier. This study aims to address the effectiveness of these LLMs on stance detection using this COVID-19 opinion corpus. The corpus is chunked and sampled to be used as input for OpenAI's GPT-3.5-Turbo LLM. The machine-generated stances are then evaluated against multiple binary classification metrics. It is shown that these models perform very well in the field of stance detection, with an average F-score of 0.895. However, a significant number of misclassifications are observed in one dataset. Therefore we conclude that while LLMs offer valuable guidelines, it is still crucial to verify their outputs when dealing with complex or important public matters.

Large Language Models (LLM) have brought significant performance increase on many Natural Language Processing tasks. However LLMs have not been tested for meeting summarization. This research paper examines the effectiveness of the gpt-3.5-turbo model on the meeting summarization domain. However due to input length limitations, it cannot be applied directly to this task. Thus the paper investigates two segmentation methods: a simple context-length window approach and topic segmentation using Latent Dirichlet Allocation (LDA). The context-length window approach's performance is close to the Pointer Generator framework. The topic segmentation gives worse results. Overall gpt-3.5-turbo performs worse with both approaches than state-of-the-art models which use a transformer architecture adapted for long documents.

Meetings are the keystone of a good company. They allow for quick decision making, multiple-perspective problem solving and effective communication. However, most employees and managers have a negative view on the efficiency and quality of their meetings. High quality meetings where every participant feels equally heard and respected is crucial for having positive meeting sentiment within a company. One of the most influential aspects of meetings are speech overlaps. Overlaps range from short utterances such as backchannels, to follow up questions and clarifications, to complete interruptions. In non-competitive cases, the overlapped speaker feels that the other participants are listening and actively engaging with them during the meeting. In competitive cases, the overlapped speaker can feel interrupted and unimportant. Therefore, competitive overlaps often have a negative impact on the course of the discussion and the overlappee's meeting sentiment. In problematic cases, these overlaps should be reduced to a minimum. In order to do this, overlaps must be classified as either competitive or non-competitive. This paper proposes a novel approach to overlap classification, namely that of text-based classification through Large Language Models. Four different prompt designs are used and tested on the two best performing and publicly available models, GPT-3.5-turbo and GPT-4. The results show that the in-context learning approach using the GPT-4 model results in the most accurate classifications. When comparing the results to previous work, it is observed that the text-based GPT-4 model matches carefully engineered neural networks that even adopt a multi-modular approach.

Behavior support applications require an understanding of the user's values to provide personalized support. User models have been developed to capture this relationship, but they may not always address unforeseen circumstances due to the changing nature of human values. To address this, the ability to update value models in real-time is crucial. This paper presents a study on the design of a graphical user interface (GUI) that utilizes behavior trees to elicit and update personal values. The study focuses on comparing the accuracy and usability of different questioning techniques, specifically comparative questions and isolation questions, and user interfaces. The results show that GUI with isolation questions and text user interface with comparative questions performed better in terms of accuracy compared to GUI with comparative questions. The study also highlights the possible impact of discrepancies in experimental procedures on the results. Furthermore, the limitations of the behavior tree model and the need for model improvements are discussed. Future work is proposed to address these limitations and explore enhancements such as alternative models, supervised processes for baseline tree building, frictionless elicitation techniques, consideration of random value shifts and big life events, and the potential of large language models. Overall, the findings contribute to the understanding of eliciting and updating user value models in personalized systems.

Involvement in an online setting has become increasingly more important in a rapidly digitizing world. Previous research has suggested that lexical alignment is an effective method for increasing interpersonal involvement. Multi-modal systems exist for analysing involvement, but the lexical alignment modality is not commonly used in these. This study examines the lexical alignment modality as a predictive measure of involvement in online group meetings. By analyzing participation indicators in virtual meetings, the aim is to identify moments of high involvement. Results indicate that lexical alignment does not correlate with moments of high involvement. The findings contribute to the scientific understanding of leveraging the verbal modality for analyzing and organizing online meetings.

Since the recent rise and advancement of video conferencing platforms such as Zoom, it has become important to interpret the logistics of remote online meetings. Analysing verbal and non-verbal cues (such as body language) between members of these virtual forums can provide additional information regarding the level of conversational involvement of each party. This research focuses on age, gender, demographics and virtual background differences in the context of group conversational discussions. It argues that groups formed of younger adults have a higher level of involvement compared to the older groups. Similarly, this study found that groups with a higher ratio of male participants score better in virtual conversational engagement compared to women preponderant groups. To better understand the influence of these inter- and intra-personal characteristics, a corpus formed of 45 online meetings on the topic of Covid-19 was used. This set of data consists of questionnaires with measurements (demographics and other personal values), as well as detailed annotations of conversational signals, which provide valuable insights into the research topic of conversational involvement.

This study includes an experiment to investigate the involvement of individual backgrounds in the prediction of group conversational engagement. Four predictive models were used, namely the Decision Tree, Random Forest, Linear Regression and Generalized Linear Mixed Effects Models. While the Generalized Linear Mixed Effects Model provides more meaningful observations on the statistical effect of these factors, the Random Forest ultimately proved to give the best performance accuracy. The purpose of this research is to improve the connection between humans and technology by studying how inter- and intra- characteristics of individuals impact the involvement of a group in virtual interaction.

Behavior support applications aim to provide personalized and flexible support to users in various domains. To achieve this, understanding users' preferences, values, and context is crucial. Creating user models that incorporate users' norms and values has been proposed as a solution to capture the relationship between desired behaviors and values. However, updating and modifying user models at run-time remains a challenge, as users' norms and values may change over time. This study investigates the accuracy of an audio interface designed to elicit values-related information using isolated questions. This involves designing an audio interface and evaluating its effectiveness through participant interactions, where they are presented with four scenarios. It was found that the audio interface performs above average in terms of usability, as indicated by the System Usability Scale score. The accuracy of the user models is evaluated through the Hamming distance and value differences between the base model and the participant-improved model. Most models required a small number of changes, and when changes were made, they were generally minimal. Additionally, feedback collected through open-ended interview questions lays down a basis for further development. The study contributes to the field by demonstrating the efficacy of the audio interface and its potential for updating user models in real-time. Overall, the research findings support the development of more effective and personalized behavior support applications that can adapt to its users.

This research paper focuses on the accuracy and limitations of user values elicited through a textual interface with questions asked in isolation. The primary objective was to conduct a user study using a textual interface that uses questions in isolation to assess the effectiveness and accuracy of this interface type and questioning style. The study involved exploring various scenarios and associated user values, as well as comparing the textual interface with graphical and audio interfaces tested in four related studies. The user study consisted of 15 participants who interacted with the textual interface. Afterwards, they were tasked with judging and adapting their behaviour models while also evaluating the interface's usability. The findings indicate that while the textual interface demonstrates decent usability, participants did not perceive a strong need for the current system and that compared to other interface types, the textual interface does not yield the most accurate results. This research provides insights into the usability and limitations of a textual interface for eliciting user values. It emphasizes the need for further exploration and development of alternative interface types to enhance accuracy and user engagement.

The alignment of behavior support systems with our personal values becomes increasingly important as behavior support systems continue to influence our daily lives. The purpose of this paper was to explore the use of graphical interfaces and isolation questions to elicit personal values and build accurate user models. An experiment was conducted in two phases to assess the accuracy and usability of the created interface. A comparison was also made with other types of interfaces developed within the research group. This experiment provided valuable insights but also held some limitations. The findings form a valuable contribution for future research and development in building responsible AI and personalized assistance from behavior support agents.

Individuals seeking a healthier lifestyle can benefit from behavior support agents. Customization and transparency are crucial for system effectiveness. This paper proposes using behavior trees as a user model, with a conversational agent extracting necessary information. The conversational interface enhances transparency, allowing users to understand how the system perceives them. Understanding comparative questions is vital to this approach's success. The objective is to investigate modeling personal values accurately using a conversational agent. Technologically literate participants engaged in iterative dialogue to elicit a personalized user model. Scenarios explored contextual factors' impact on value alignment. Results revealed decreased accuracy when more values were affected by contextual factors. Comparative questions were less effective than isolated questioning. System usability was rated poor but approaching acceptability. Larger sample sizes are needed for comprehensive conclusions. This research lays the foundation for conversational agents that model personal values within behavior trees, advancing behavior support systems.

With the rise in popularity of online conferencing platforms such as Zoom and Teams, there is an increasing need to understand the dynamics of virtual meetings to enhance productivity. One key aspect that can contribute to productive online meetings is conversational involvement. Personality traits influence the behaviour of participants within a group and therefore, have the potential to influence conversational involvement. This research paper aims to explore the impact of personality traits on conversational involvement in online meetings. To interpret personalities, the HEXACO model has been employed. Through a systematic investigation, this study provides valuable insights into how individual personalities influence engagement levels in a group during virtual meetings. The results of this study shed light on the relationship between personality traits and conversational involvement, thus contributing to the existing body of knowledge surrounding effective online meeting practices.

The importance of understanding how to maximize involvement in virtual meetings has been greatly increased due to the rapid rise of video conferencing tools during the COVID-19 pandemic. This research builds upon the data collected by the MEMO Corpus, specifically interview footage and annotated data, and will aim to explore the various factors that influence group involvement in virtual meetings. The research will be conducted in three steps; data annotation, data exploration, and data analysis. The goal of this paper is to investigate the influence of factors such as likeability, listening, and audience engagement in how conversational ability is perceived, given that the scales provided by the MEMOCorpus are valid and the three measures are independent, and how conversational ability influences group involvement, by exploring the existence of correlations. The results of the analysis will provide insight into the dynamics of conversational involvement in virtual meetings and improve virtual communication practices.

Negotiation Support Systems (NSSs) can provide help based on the preference setting (domain, issue weights, issue ranking, strategies, etc.) of the users of the systems. However, sometimes the users of the systems might make mistakes in the preference setting. With wrong preferences, the NSSs might provide suggestions that conflict with the users' desires. This thesis focuses on designing a mechanism that could detect the mismatches in issue weights of the users of NSSs and provide recommendations on updating the issue weights with a scoring method. This mechanism also has the ability to provide different recommendations based on the users' self-confidence level. A case study is done with six simulation experiments to test the mechanism. The results show that during the negotiation, the users of the NSS (PN) can have a chance to change the issue weights when mismatches occurred and the NSS (PN) can provide better suggestions after the issue weights are updated. The limitations of the mechanism are concluded at the end together with the future thoughts

Negotiation is not a skill that comes naturally to most people. However, most people could benefit from attaining good negotiation skills. Non-verbal behaviour plays an important role in negotiations. Previous studies have shown a link between mimicry through conversational agents and self-regulation of non-verbal behaviour. The current study aims to raise a person’s self-awareness of their non-verbal behaviour through the medium of a conversational agent in negotiation training. An experiment (N = 64) is conducted where participants performed negotiations with an intermediate feedback round where the participants' non-verbal behaviour was played back on a virtual agent. The agent either mimicked the participants’ non-verbal behaviour or exhibited idle behaviour. It was found that the mimicking agent provided better feedback to the participants. Furthermore, repeated negotiations increase a person's self-awareness. The data indicate that raising a person’s self-awareness in negotiation training by utilising non-verbal mimicry through a conversational agent is feasible.

This research set out to find whether the use of gestures by virtual embodied agents could affect perceptions of integrity by humans. 48 participants played a quiz with the assistance of a gesturing and an idle agent. Likert scales were used to evaluate their perceptions of integrity, which showed no significant difference between either agent. Interestingly, the actions of the participants showed something different: participants chose the gesturing agent to provide assistance a significantly higher number of times than the idle agent. One possible explanation for this result is that even though participants did not consciously notice a difference between the two agents, they subconsciously did while performing the quiz task. Another explanation is that choosing the gesturing agent more did not have to do with perceived integrity, but rather perceived ability, benevolence, or something else entirely.