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- ZeitschriftenartikelAnalysis 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, SebastianDiscourse 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.
- ZeitschriftenartikelAnswering Comparative Questions with Arguments(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Bondarenko, Alexander; Panchenko, Alexander; Beloucif, Meriem; Biemann, Chris; Hagen, MatthiasQuestion 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.
- ZeitschriftenartikelArgumenText: 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, IrynaThe 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.
- ZeitschriftenartikelEditorial(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Cimiano, Philipp; Heyer, Gerhard; Kohlhase, Michael; Stein, Benno; Ziegler, Jürgen; Härder, Theo
- ZeitschriftenartikelExplaining Arguments with Background Knowledge(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Becker, Maria; Hulpuş, Ioana; Opitz, Juri; Paul, Debjit; Kobbe, Jonathan; Stuckenschmidt, Heiner; Frank, AnetteMost 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.
- ZeitschriftenartikelHow to Win Arguments(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Weber, Klaus; Rach, Niklas; Minker, Wolfgang; André, ElisabethPeople 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.
- ZeitschriftenartikelLeveraging Arguments in User Reviews for Generating and Explaining Recommendations(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Donkers, Tim; Ziegler, JürgenReview 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.
- ZeitschriftenartikelNews(Datenbank-Spektrum: Vol. 20, No. 2, 2020)
- ZeitschriftenartikelReconstructing Arguments from Noisy Text(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Dykes, Natalie; Evert, Stefan; Göttlinger, Merlin; Heinrich, Philipp; Schröder, LutzSocial 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.
- ZeitschriftenartikelRelational and Fine-Grained Argument Mining(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Trautmann, Dietrich; Fromm, Michael; Tresp, Volker; Seidl, Thomas; Schütze, HinrichIn our project ReMLAV , funded within the DFG Priority Program RATIO ( http://www.spp-ratio.de/ ), we focus on relational and fine-grained argument mining. In this article, we first introduce the problems we address and then summarize related work. The main part of the article describes our research on argument mining, both coarse-grained and fine-grained methods, and on same-side stance classification, a relational approach to the problem of stance classification. We conclude with an outlook.