@misc{34025, abstract = {{Controversial topics like abortion or capital punishment inherently lack of correct answers or the right way to deal with. Thus, in order to find what is true, what is good, or what should be done, the involved parties need to debate. For the purpose of forming an opinion on a controversial topic someone needs to take in a lot of arguments on that topic to gather information which can be a time-consuming process. To increase efficiency, someone can use an argument search engine to quicken the retrieval of relevant arguments. Although the usage of such a service reduces the time to find arguments, there is still a lot of textual data that needs to be read. To this end, computational summarization approaches for arguments can limit the necessary time for information review by generating short snippets capturing the main gist of each argument. Yet, we suggest that approaches that consider one argument at a time show potential for further improvement in terms of efficiency during information review. In fact, arguments on the same topic, like those retrieved by a search engine for a certain query, partially cover the same content, e. g. arguments regarding the death penalty probably use deterrence as a point in favor of it. However, if the same aspect is central in multiple arguments, their snippets reflect this, which leads to redundancy among the snippets. Consequently, someone interested in gathering information on a controversial topic does not necessarily find new information in each snippet he or she reads. We introduce the task of Contrastive Argument Summarization (CAS) which addresses the aforementioned problem regarding existing argument summarization. An approach that addresses CAS aims to produce contrastive snippets for each argument in a set of topic-related arguments. A contrastive snippet should represent the main gist of its argument, it should account for the argumentative nature of the text, and it should be dissimilar to the other topic-related arguments in order to reduce redundancy among the snippets. We propose two approaches addressing CAS, namely an extended version of the LexRank derivation by Alshomary et al. (2020), and an advancement of the work by Bista et al. (2020). Additionally, we develop two automatic measures to assess to which extent the snippets of one set are opposed. For evaluation, we compile a corpus using the args.me search engine Wachsmuth et al. (2017b) to come close to the suggested area of application. Moreover, we conduct a manual annotation study to assess approaches’ effectiveness. We find that the graph-based approach is superior when it comes to contrastiveness (i. e. snippets being dissimilar to topic-related arguments), and that the second approach outperforms the previous one and the unmodified version of Alshomary et al. (2020) when it comes to representativeness (i. e. snippets capturing the main gist of an argument).}}, author = {{Rieskamp, Jonas}}, title = {{{Contrastive Argument Summarization Using Supervised and Unsupervised Machine Learning}}}, year = {{2022}}, } @inproceedings{34040, abstract = {{Consider the practical goal of making a desired action profile played, when the planner can only change the payoffs, bound by stringent constraints. Applications include motivating people to choose the closest school, the closest subway station, or to coordinate on a communication protocol or an investment strategy. Employing subsidies and tolls, we adjust the game so that choosing this predefined action profile becomes strictly dominant. Inspired mainly by the work of Monderer and Tennenholtz, where the promised subsidies do not materialise in the not played profiles, we provide a fair and individually rational game adjustment, such that the total outside investments sum up to zero at any profile, thereby facilitating easy and frequent usage of our adjustment without bearing costs, even if some players behave unexpectedly. The resultant action profile itself needs no adjustment. Importantly, we also prove that our adjustment minimises the general transfer among all such adjustments, counting the total subsidising and taxation.}}, author = {{Polevoy, Gleb and Dziubiński, Marcin}}, booktitle = {{Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence}}, editor = {{De Raedt, Luc}}, keywords = {{adjustment, strictly dominant, fairness, individually rational, transfer, tax, subsidy}}, location = {{Vienna}}, publisher = {{International Joint Conferences on Artificial Intelligence Organization}}, title = {{{Fair, Individually Rational and Cheap Adjustment}}}, doi = {{10.24963/ijcai.2022/64}}, year = {{2022}}, } @article{30105, abstract = {{Zusammenfassung: Der Beitrag befasst sich mit der professionellen pädagogischen Beziehung zwischen Referendar*innen und ihren Seminarlehrkräften, deren Beitrag für die Beanspruchung der Referendar*innen bisher noch nicht eingängig untersucht wurde. Das Ziel der Studie ist es, anhand einer Querschnittserhebung von 2583 Referendar*innen und ausgebildeten Lehrkräften, kompensatorische sowie verstärkende Effekte der Beziehungsdimensionen Transparenz, Fairness, Vertrauen und Ambivalenz auf die wahrgenommene Beanspruchung im Referendariat zu untersuchen. Die Analyse erfolgt mittels eines latent moderierten Strukturgleichungsansatzes. Die Ergebnisse zeigen, dass Belastungen durch die Kerntätigkeiten im Referendariat sowie Belastungen durch den Umgang mit Kolleg*innen im Lehrer*innenkollegium in signifikant positivem Zusammenhang mit resultierenden Beanspruchungsreaktionen stehen. Je transparenter, fairer und vertrauensvoller und je weniger ambivalent die Beziehung zur Seminarlehrkraft erlebt wird, desto geringer fallen die Beanspruchungsreaktionen aus. Die Ergebnisse werden hinsichtlich ihrer Relevanz für die Beziehungsarbeit im Referendariat als zentralem Ausbildungsbestandteil der zweiten Phase der Lehrer*innenausbildung diskutiert.}}, author = {{Kärner, Tobias and Goller, Michael and Bonnes, Caroline and Maué, Elisabeth}}, issn = {{1434-663X}}, journal = {{Zeitschrift für Erziehungswissenschaft}}, keywords = {{Education}}, pages = {{687--719}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Die professionelle pädagogische Beziehung zwischen Referendar*innen und ihren Seminarlehrkräften: Belastungsfaktor oder Ressource?}}}, doi = {{10.1007/s11618-022-01065-4}}, volume = {{25}}, year = {{2022}}, } @inproceedings{33085, author = {{Epstein, Leah and Lassota, Alexandra and Levin, Asaf and Maack, Marten and Rohwedder, Lars}}, booktitle = {{39th International Symposium on Theoretical Aspects of Computer Science, STACS 2022, March 15-18, 2022, Marseille, France (Virtual Conference)}}, editor = {{Berenbrink, Petra and Monmege, Benjamin}}, pages = {{28:1–28:15}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Cardinality Constrained Scheduling in Online Models}}}, doi = {{10.4230/LIPIcs.STACS.2022.28}}, volume = {{219}}, year = {{2022}}, } @article{34044, author = {{Hoffmann, Christin and Thommes, Kirsten}}, issn = {{0959-6526}}, journal = {{Journal of Cleaner Production}}, keywords = {{Industrial and Manufacturing Engineering, Strategy and Management, General Environmental Science, Renewable Energy, Sustainability and the Environment, Building and Construction}}, publisher = {{Elsevier BV}}, title = {{{Clear Roads and Dirty Air? Indirect effects of reduced private traffic congestion on emissions from heavy traffic}}}, doi = {{10.1016/j.jclepro.2022.135053}}, year = {{2022}}, } @article{34045, author = {{Hoffmann, Christin and Thommes, Kirsten}}, issn = {{0959-6526}}, journal = {{Journal of Cleaner Production}}, keywords = {{Industrial and Manufacturing Engineering, Strategy and Management, General Environmental Science, Renewable Energy, Sustainability and the Environment, Building and Construction}}, publisher = {{Elsevier BV}}, title = {{{Clear Roads and Dirty Air? Indirect effects of reduced private traffic congestion on emissions from heavy traffic}}}, doi = {{10.1016/j.jclepro.2022.135053}}, year = {{2022}}, } @inproceedings{30840, author = {{Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}}, pages = {{8782 -- 8797}}, title = {{{The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments}}}, year = {{2022}}, } @inproceedings{33004, author = {{Wachsmuth, Henning and Alshomary, Milad}}, booktitle = {{Proceedings of the 29th International Conference on Computational Linguistics}}, pages = {{344 -- 354}}, title = {{{"Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain}}}, year = {{2022}}, } @article{34049, author = {{Lauscher, Anne and Wachsmuth, Henning and Gurevych, Iryna and Glavaš, Goran}}, journal = {{Transactions of the Association for Computational Linguistics}}, title = {{{On the Role of Knowledge in Computational Argumentation}}}, year = {{2022}}, } @inproceedings{22157, author = {{Kiesel, Johannes and Alshomary, Milad and Handke, Nicolas and Cai, Xiaoni and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}}, pages = {{4459 -- 4471}}, title = {{{Identifying the Human Values behind Arguments}}}, year = {{2022}}, } @inproceedings{34057, author = {{Pasic, Faruk and Becker, Matthias}}, booktitle = {{2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)}}, publisher = {{IEEE}}, title = {{{Domain-specific Language for Condition Monitoring Software Development}}}, doi = {{10.1109/etfa52439.2022.9921730}}, year = {{2022}}, } @inbook{34027, author = {{Somogyi, Peter}}, booktitle = {{Troja bauen}}, editor = {{Federow, Anne-Katrin and Malcher, Kay}}, publisher = {{Universitätsverlag Winter}}, title = {{{"Swer siner kunst meister ist, der hat gewalt an siner list". Herborts von Fritzlar Liet von Troye als Fortsetzungstext des Eneasromans Heinrichs von Veldeke am Schnittpunkt von Aneignung und Neukonstitution}}}, year = {{2022}}, } @inproceedings{34047, abstract = {{News articles both shape and reflect public opinion across the political spectrum. Analyzing them for social bias can thus provide valuable insights, such as prevailing stereotypes in society and the media, which are often adopted by NLP models trained on respective data. Recent work has relied on word embedding bias measures, such as WEAT. However, several representation issues of embeddings can harm the measures' accuracy, including low-resource settings and token frequency differences. In this work, we study what kind of embedding algorithm serves best to accurately measure types of social bias known to exist in US online news articles. To cover the whole spectrum of political bias in the US, we collect 500k articles and review psychology literature with respect to expected social bias. We then quantify social bias using WEAT along with embedding algorithms that account for the aforementioned issues. We compare how models trained with the algorithms on news articles represent the expected social bias. Our results suggest that the standard way to quantify bias does not align well with knowledge from psychology. While the proposed algorithms reduce the~gap, they still do not fully match the literature.}}, author = {{Spliethöver, Maximilian and Keiff, Maximilian and Wachsmuth, Henning}}, booktitle = {{Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)}}, location = {{Abu Dhabi}}, publisher = {{Association for Computational Linguistics}}, title = {{{No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media}}}, year = {{2022}}, } @inbook{34017, author = {{Musiol, Marie-Luise and Winst, Silke}}, booktitle = {{Spiritual Vegetation: Vegetal Nature in Religious Contexts Across Medieval and Early Modern Europe (Berliner Mittelalter- und Frühneuzeitforschung, Vol. 26)}}, editor = {{Lamsechi, Guita and Trînca, Beatrice}}, pages = {{97--129}}, title = {{{Pfirsichbaum und dunkler Wald. Pflanzliche Konfigurationen zwischen Dynamisierung und Innehalten in 'Partonopier und Meliur' Konrads von Würzburg}}}, year = {{2022}}, } @misc{34019, author = {{Musiol, Marie-Luise}}, booktitle = {{Sexuologie}}, title = {{{Bennewitz, Ingrid/Eming, Jutta/Traulsen, Johannes: Gender Studies – Queer Studies – Intersektionalität. Eine Zwischenbilanz aus mediävistischer Perspektive (Berliner Mittelalter- und Frühneuzeitforschung 25), Göttingen 2019}}}, year = {{2022}}, } @inbook{34018, author = {{Musiol, Marie-Luise}}, booktitle = {{Unverfügbar. Kulturen des Heiligen}}, editor = {{Egidi, Margreth and Peters, Ludmilla and Schmidt, Jochen}}, title = {{{Menschlicher Körper, heiliger Körper. Zur Kreuzigung Christi im Donaueschinger Passionsspiel}}}, year = {{2022}}, } @article{29049, abstract = {{This study investigates the conditions under which tax rate changes accelerate risky investments. While tax rate increases are often expected to harm investment, analytical studies find tax rate increases may foster investment under flexibility.We design a theorybased experimentwith a binomial random walk and entry–exit flexibility.We find accelerated investment upon tax rate increases irrespective of an exit option, but no corresponding response to tax cuts. This asymmetry may be due to tax salience and mechanisms from irreversible choice under uncertainty. Given this evidence of unexpected tax-reform effects, tax policymakers should carefully consider behavioral aspects.}}, author = {{Fahr, René and Janssen, Elmar A. and Sureth-Sloane, Caren}}, journal = {{FinanzArchiv / Public Finance Analysis}}, keywords = {{Economic ExperimentM, Investment Decisions, Tax Effects, Timing Flexibility, Uncertainty}}, number = {{1-2}}, pages = {{239--289}}, title = {{{Can Tax Rate Changes Accelerate Investment under Entry and Exit Flexibility? – Insights from an Economic Experiment}}}, volume = {{78}}, year = {{2022}}, } @article{29048, abstract = {{We study the bargaining behavior between auditor and auditee in a tax setting and scrutinize the effect of interpersonal trust and trust in government on both parties’ concessions. We find evidence that both kinds of trust affect the concessionary behavior, albeit in different ways. While trust in government affects concessionary behavior in line with intuitive predictions, we find that interpersonal trust only affects tax auditors. For high interpersonal trust, the alleviating effect of high trust in government on tax auditors’ concessions is less pronounced. Our findings help tax authorities to shape programs to enhance compliance in an atmosphere of trust.}}, author = {{Eberhartinger, Eva and Speitmann, Raffael and Sureth-Sloane, Caren and Wu, Yuchen}}, journal = {{FinanzArchiv / Public Finance Analysis}}, keywords = {{Behavioral Taxation, Concessionary Behavior, Interpersonal Trust, Tax Audit, Trust in Government}}, number = {{1-2}}, pages = {{112--155}}, title = {{{How Does Trust Affect Concessionary Behavior in Tax Bargaining?}}}, volume = {{78}}, year = {{2022}}, } @inproceedings{31331, author = {{Hetkämper, Tim and Claes, Leander and Henning, Bernd}}, booktitle = {{Sensoren und Messsysteme - Beiträge der 21. ITG/GMA-Fachtagung}}, isbn = {{978-3-8007-5835-7}}, location = {{Nürnberg}}, publisher = {{VDE Verlag GmbH}}, title = {{{Schlieren imaging with fractional Fourier transform to visualise ultrasonic fields}}}, year = {{2022}}, } @inproceedings{34067, author = {{Sengupta, Meghdut and Alshomary, Milad and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 2022 Workshop on Figurative Language Processing}}, title = {{{Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning}}}, year = {{2022}}, }