38,524 research outputs found
What changed your mind : the roles of dynamic topics and discourse in argumentation process
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the in- creasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful -- topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations
Meetings and Meeting Modeling in Smart Environments
In this paper we survey our research on smart meeting rooms and its relevance for augmented reality meeting support and virtual reality generation of meetings in real time or off-line. The research reported here forms part of the European 5th and 6th framework programme projects multi-modal meeting manager (M4) and augmented multi-party interaction (AMI). Both projects aim at building a smart meeting environment that is able to collect multimodal captures of the activities and discussions in a meeting room, with the aim to use this information as input to tools that allow real-time support, browsing, retrieval and summarization of meetings. Our aim is to research (semantic) representations of what takes place during meetings in order to allow generation, e.g. in virtual reality, of meeting activities (discussions, presentations, voting, etc.). Being able to do so also allows us to look at tools that provide support during a meeting and at tools that allow those not able to be physically present during a meeting to take part in a virtual way. This may lead to situations where the differences between real meeting participants, human-controlled virtual participants and (semi-) autonomous virtual participants disappear
Pro-active Meeting Assistants : Attention Please!
This paper gives an overview of pro-active meeting assistants, what they are and when they can be useful. We explain how to develop such assistants with respect to requirement definitions and elaborate on a set of Wizard of Oz experiments, aiming to find out in which form a meeting assistant should operate to be accepted by participants and whether the meeting effectiveness and efficiency can be improved by an assistant at all
Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches
Quantitative methods to measure the participation to parliamentary debate and
discourse of elected Members of Parliament (MPs) and the parties they belong to
are lacking. This is an exploratory study in which we propose the development
of a new approach for a quantitative analysis of such participation. We utilize
the New Zealand government's digital Hansard database to construct a topic
model of parliamentary speeches consisting of nearly 40 million words in the
period 2003-2016. A Latent Dirichlet Allocation topic model is implemented in
order to reveal the thematic structure of our set of documents. This generative
statistical model enables the detection of major themes or topics that are
publicly discussed in the New Zealand parliament, as well as permitting their
classification by MP. Information on topic proportions is subsequently analyzed
using a combination of statistical methods. We observe patterns arising from
time-series analysis of topic frequencies which can be related to specific
social, economic and legislative events. We then construct a bipartite network
representation, linking MPs to topics, for each of four parliamentary terms in
this time frame. We build projected networks (onto the set of nodes represented
by MPs) and proceed to the study of the dynamical changes of their topology,
including community structure. By performing this longitudinal network
analysis, we can observe the evolution of the New Zealand parliamentary topic
network and its main parties in the period studied.Comment: 28 pages, 12 figures, 3 table
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