Datenbank Spektrum 20(2) - Juli 2020

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  • Zeitschriftenartikel
    Reconstructing Arguments from Noisy Text
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Dykes, Natalie; Evert, Stefan; Göttlinger, Merlin; Heinrich, Philipp; Schröder, Lutz
    Social media are of paramount importance to public discourse. RANT aims to contribute methods and formalisms for extracting, representing, and processing arguments from noisy text found in social media discussions, using a large corpus of pre-referendum Brexit tweets as a running case study. We identify recurring linguistic argumentation patterns in a corpus-linguistic analysis and formulate corresponding corpus queries to extract arguments automatically. Given the huge amount of social media data available, our approach aims at high precision at the possible price of low recall. Argumentation patterns are directly associated with logical patterns in a dedicated formalism and accordingly, individual arguments are directly parsed as logical formulae. The logical formalism for argument representation features a broad range of modalities capturing real-life modes of expression. We cast this formalism as a family of instance logics in the generic framework of coalgebraic logic and complement it by a flexible framework to represent relationships between arguments; including standard relations like attack and support but also relations extracted from metadata. Some relations are inferred from the logical content of individual arguments. We are in the process of developing suitable generalizations of various extension semantics for argumentation frameworks combined with corresponding algorithmic methods to allow for the automated retrieval of large-scale argumentative positions.
  • Zeitschriftenartikel
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020)
  • Zeitschriftenartikel
    The ReCAP Project
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Bergmann, Ralph; Biertz, Manuel; Dumani, Lorik; Lenz, Mirko; Ludwig, Anna-Katharina; Neumann, Patrick J.; Ollinger, Stefan; Sahitaj, Premtim; Schenkel, Ralf; Witry, Alex
    Argumentation Machines search for arguments in natural language from information sources on the Web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. The recap project is part of the Priority Program ratio and aims at novel contributions to and confluence of methods from information retrieval, knowledge representation, as well as case-based reasoning for the development of future argumentation machines. In this paper we summarise recent research results from the project. In particular, a new German corpus of 100 semantically annotated argument graphs from the domain of education politics has been created and is made available to the argumentation research community. Further, we discuss a comprehensive investigation in finding arguments and argument graphs. We introduce a probabilistic ranking framework for argument retrieval, i.e. for finding good premises for a designated claim. For finding argument graphs, we developed methods for case-based argument retrieval considering the graph structure of an argument together with textual and ontology-based similarity measures applied to claims, premises, and argument schemes.
  • Zeitschriftenartikel
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Cimiano, Philipp; Heyer, Gerhard; Kohlhase, Michael; Stein, Benno; Ziegler, Jürgen; Härder, Theo
  • Zeitschriftenartikel
    Towards Understanding and Arguing with Classifiers: Recent Progress
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Shao, Xiaoting; Rienstra, Tjitze; Thimm, Matthias; Kersting, Kristian
    Machine learning and argumentation can potentially greatly benefit from each other. Combining deep classifiers with knowledge expressed in the form of rules and constraints allows one to leverage different forms of abstractions within argumentation mining. Argumentation for machine learning can yield argumentation-based learning methods where the machine and the user argue about the learned model with the common goal of providing results of maximum utility to the user. Unfortunately, both directions are currently rather challenging. For instance, combining deep neural models with logic typically only yields deterministic results, while combining probabilistic models with logic often results in intractable inference. Therefore, we review a novel deep but tractable model for conditional probability distributions that can harness the expressive power of universal function approximators such as neural networks while still maintaining a wide range of tractable inference routines. While this new model has shown appealing performance in classification tasks, humans cannot easily understand the reasons for its decision. Therefore, we also review our recent efforts on how to “argue” with deep models. On synthetic and real data we illustrate how “arguing” with a deep model about its explanations can actually help to revise the model, if it is right for the wrong reasons.
  • Zeitschriftenartikel
    Analysis of Political Debates through Newspaper Reports: Methods and Outcomes
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Lapesa, Gabriella; Blessing, Andre; Blokker, Nico; Dayanik, Erenay; Haunss, Sebastian; Kuhn, Jonas; Padó, Sebastian
    Discourse network analysis is an aspiring development in political science which analyzes political debates in terms of bipartite actor/claim networks. It aims at understanding the structure and temporal dynamics of major political debates as instances of politicized democratic decision making. We discuss how such networks can be constructed on the basis of large collections of unstructured text, namely newspaper reports. We sketch a hybrid methodology of manual analysis by domain experts complemented by machine learning and exemplify it on the case study of the German public debate on immigration in the year 2015. The first half of our article sketches the conceptual building blocks of discourse network analysis and demonstrates its application. The second half discusses the potential of the application of NLP methods to support the creation of discourse network datasets.
  • Zeitschriftenartikel
    Explaining Arguments with Background Knowledge
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Becker, Maria; Hulpuş, Ioana; Opitz, Juri; Paul, Debjit; Kobbe, Jonathan; Stuckenschmidt, Heiner; Frank, Anette
    Most information we consume as a society is obtained over the Web. News – often from questionable sources – are spread online, as are election campaigns; calls for (collective) action spread with unforeseen speed and intensity. All such actions have argumentation at their core, and the conveyed content is often strategically selected or rhetorically framed. The responsibility of critical analysis of arguments is thus tacitly transferred to the content consumer who is often not prepared for the task, nor aware of the responsibility. The ExpLAIN project aims at making the structure and reasoning of arguments explicit – not only for humans, but for Robust Argumentation Machines that are endowed with language understanding capacity. Our vision is a system that is able to deeply analyze argumentative text: that identifies arguments and counter-arguments, and reveals their internal structure, conveyed content and reasoning. A particular challenge for such a system is to uncover implicit knowledge which many arguments rely on. This requires human background knowledge and reasoning capacity, in order to explicate the complete reasoning of an argument. This article presents ongoing research of the ExpLAIN project that aims to make the vision of such a system a tangible aim. We introduce the problems and challenges we need to address, and present the progress we achieved until now by applying advanced natural language and knowledge processing methods. Our approach puts particular focus on leveraging available sources of structured and unstructured background knowledge, the automatic extension of such knowledge, the uncovering of implicit content, and reasoning techniques suitable for informal, everyday argumentation.
  • Zeitschriftenartikel
    Leveraging Arguments in User Reviews for Generating and Explaining Recommendations
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Donkers, Tim; Ziegler, Jürgen
    Review texts constitute a valuable source for making system-generated recommendations both more accurate and more transparent. Reviews typically contain statements providing argumentative support for a given item rating that can be exploited to explain the recommended items in a personalized manner. We propose a novel method called Aspect-based Transparent Memories (ATM) to model user preferences with respect to relevant aspects and compare them to item properties to predict ratings, and, by the same mechanism, explain why an item is recommended. The ATM architecture consists of two neural memories that can be viewed as arrays of slots for storing information about users and items. The first memory component encodes representations of sentences composed by the target user while the second holds an equivalent representation for the target item based on statements of other users. An offline evaluation was performed with three datasets, showing advantages over two baselines, the well-established Matrix Factorization technique and a recent competitive representative of neural attentional recommender techniques.
  • 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.
  • Zeitschriftenartikel
    ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Daxenberger, Johannes; Schiller, Benjamin; Stahlhut, Chris; Kaiser, Erik; Gurevych, Iryna
    The ArgumenText project creates argument mining technology for big and heterogeneous data and aims to evaluate its use in real-world applications. The technology mines and clusters arguments from a variety of textual sources for a large range of topics and in multiple languages. Its main strength is its generalization to very different textual sources including web crawls, news data, or customer reviews. We validated the technology with a focus on supporting decisions in innovation management as well as customer feedback analysis. Along with its public argument search engine and API, ArgumenText has released multiple datasets for argument classification and clustering. This contribution outlines the major technology-related challenges and proposed solutions for the tasks of argument extraction from heterogeneous sources and argument clustering. It also lays out exemplary industry applications and remaining challenges.