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Datenbank Spektrum 20(2) - Juli 2020

https://dl.gi.de/handle/20.500.12116/36362
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Auflistung Datenbank Spektrum 20(2) - Juli 2020 nach Schlagwort "Argumentation"

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  • Zeitschriftenartikel
    Answering Comparative Questions with Arguments
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Bondarenko, Alexander; Panchenko, Alexander; Beloucif, Meriem; Biemann, Chris; Hagen, Matthias
    Question answering platforms such as Yahoo! Answers or Quora always contained questions that ask other humans for help when comparing two or more options. Since nowadays more and more people also “talk” to their devices, such comparative questions are also part of the query stream that major search engines receive. Interestingly, major search engines answer some comparative questions pretty well while for others, they just show the “standard” ten blue links. But a good response to a comparative question might be very different from these ten blue links—for example, a direct answer could show an aggregation of the pros and cons of the different options. This observation motivated our DFG-funded project “ACQuA: Answering Comparative Questions with Arguments” for which we describe the achieved results so far, and ongoing activities like the first shared task on argument retrieval.
  • Zeitschriftenartikel
    How to Win Arguments
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Weber, Klaus; Rach, Niklas; Minker, Wolfgang; André, Elisabeth
    People make decisions every day or form an opinion based on persuasion processes, whether through advertising, planning leisure activities with friends or public speeches. Most of the time, however, subliminal persuasion processes triggered by behavioral cues (rather than the content of the message) play a far more important role than most people are aware of. To raise awareness of the different aspects of persuasion ( how and what ), we present a multimodal dialog system consisting of two virtual agents that use synthetic speech in a discussion setting to present pros and cons to a user on a controversial topic. The agents are able to adapt their emotions based on explicit feedback of the users to increase their perceived persuasiveness during interaction using Reinforcement Learning.