2024

Actor Identification in Discourse: A Challenge for LLMs?.
Barić A, Papay S, and Padó S. In arXiv preprint arXiv:2402.00620, Feb 1, 2024.

Hypothesis Description: Enemy Release Hypothesis.
Heger, T., Jeschke, J. M., Bernard-Verdier, M., Musseau, C. L. and Mietchen, D. In Research Ideas and Outcomes, 10. (2024).

Template for a Hypothesis Description paper.
Heger, T., Mietchen, D. and Jeschke, J. M. In Research Ideas and Outcomes, 10. (2024).

Introducing Hypothesis Descriptions.
Mietchen, D., Jeschke, J. M., & Heger, T. In Research Ideas and Outcomes, 10, e119805. (2024).

Generic Model Checking for Modal Fixpoint Logics in COOL-MC
Hausmann, D., Humml, M., Prucker, S., Schröder, L., & Strahlberger, A. In Rayna Dimitrova, Ori Lahav, Sebastian Wolff (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 171-185). London, GB: Springer Science and Business Media Deutschland GmbH. (2024).

Argumentation Schemes for Blockchain Deanonymisation.
Deuber, D., Gruber, J., Humml, M., Ronge, V., & Scheler, N. In FinTech, 3, 236-248. (2024).

Generic Model Checking for Modal Fixpoint Logics in COOL-MC
Hausmann, D., Humml, M., Prucker, S., Schröder, L., and Strahlberger, A. In Rayna Dimitrova, Ori Lahav, Sebastian Wolff (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 171-185). London, GB: Springer Science and Business Media Deutschland GmbH. (2024).

Argumentation Schemes for Blockchain Deanonymisation.
Deuber, D., Gruber, J., Humml, M., Ronge, V., and Scheler, N. In FinTech, 3, 236-248. (2024)

cPro: Circular Projections Using Gradient Descent. In EuroVA 2024.
Buchmüller, R., Jäckl, B., Behrisch, M., Keim D. A. & Dennig, F. (2024).

Exploration of Preference Models using Visual Analytics. In MLVis 2024.
Buchmüller, R., Zymla, M., Butt, M., Keim D. A., and Sevastjanova, R. (2024).

2023

A weakly-supervised learning approach to the identification of “alternative lexicalizations” in shallow discourse parsing.
René Knaebel. In Proc. of the 4th Workshop on Computational Approaches to Discourse (CODI 2023), 61–69. Association for Computational Linguistics. Toronto, Canada, July 2023.

Discourse sense flows: Modelling the rhetorical style of documents across various domains.
Rene Knaebel and Manfred Stede. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Findings of the Association for Computational Linguistics: EMNLP 2023, 14462–14482. Association for Computational Linguistics. Singapore, December 2023.

Towards Fine-Grained Argumentation Strategy Analysis in Persuasive Essays.
Robin Schaefer, René Knaebel, and Manfred Stede. In Milad Alshomary, Chung-Chi Chen, Smaranda Muresan, Joonsuk Park, and Julia Romberg, editors, Proc. of the 10th Workshop on Argument Mining, 76–88. Association for Computational Linguistics. Singapore, December 2023.

Bipolar Abstract Dialectical Frameworks Are Covered by Kleene's Three-valued Logic.
Baumann, R., Heinrich, M. In IJCAI 2023, 3123-3131. (2023).

Common Knowledge of Abstract Groups.
Humml, M. and Schröder, L. In Proc. of the Thirty-Seventh AAAI Conf. on Artificial Intelligence Thirty-Fifth Conf. on Innovative Applications of Artificial Intelligence Thirteenth Symposium on Educational Advances in Artificial Intelligence (pp. 6434-6441). Washington DC, US, 2023.

COOL 2 – A Generic Reasoner for Modal Fixpoint Logics (System Description).
Görlitz, O., Hausmann, D., Humml, M., Pattinson, D., Prucker, S., and Schröder, L. In Brigitte Pientka, Cesare Tinelli (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 234-247). Rome, IT: Springer Science and Business Media Deutschland GmbH., 2023.

Formal Verification of Necessary and Sufficient Evidence in Forensic Event Reconstruction.
Gruber, J., Humml, M., Schröder, L., and Freiling, F. In Edita Bajramovic and Ricardo J. Rodríguez (Eds.), Proc. of the Digital Forensics Research Conference Europe (DFRWS EU) (pp. 1-11). Bonn, DE, 2023.

A Formal Treatment of Expressiveness and Relevance of Digital Evidence.
Gruber, J. and Humml, M. Digital Threats: Research and Practice, 2023.

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine.
Rieger, J., Yanchenko, K., Ruckdeschel, M., von Nordheim, G., Königslöw, K. K. V., and Wiedemann, G.. In arXiv preprint arXiv:2312.16975, 2023.

Argument Quality Prediction for Ranking Documents.
Plenz M, Buchmüller R, Bondarenko A. In Working Notes of the CLEF 2023 Evaluation Labs (pp. 3119-3130), Thessaloniki, Greece, 2023.

Additive manifesto decomposition: A policy domain aware method for understanding party positioning.
Ceron, T., Nikolaev, D. and Padó, S. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7874–7890, Toronto, Canada. Association for Computational Linguistics, 2023.

Between welcome culture and border fence.
Blokker, N., Blessing, A., Dayanik, E. et al. In Language Resources and Evaluation v.57, pp. 121–153, 2023.

Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers.
Nikolaev, D., Ceron, T. and Padó, S. In Proceedings of the 2023 Conf. on Empirical Methods in Natural Language Processing, pp. 9497–9511, Singapore. Association for Computational Linguistics, 2023.

Political claim identification and categorization in a multilingual setting: First experiments.
Zaberer, U., Padó, S. and Lapesa, G. In arXiv preprint arXiv:2310.09256, 2023.

Stance-Aware Re-Ranking for Non-factual Comparative Queries.
Reimer, JH, Bondarenko, A., Fröbe, M. and Hagen, M. In Proc. of the 10th Workshop on Argument Mining, pages 45–51, Singapore. Association for Computational Linguistics. 2023.

Overview of Touché 2023: Argument and Causal Retrieval.
Bondarenko, A. et al. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163, pages 507-530, Springer, 2023.

Consumer Health Question Answering Using Off-the-Shelf Components.
Pugachev, A., Artemova, E., Bondarenko, A., Braslavski, P. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981, pages 571-579. Springer, 2023.

Predicting Terms in IS-A Relations with Pre-trained Transformers.
Nikishina, I., Chernomorchenko, P., Demidova, A., Panchenko, A., Biemann, C. In Proceedings of the 13th Intl. Joint Conf. on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, Bali, Indonesia, 2023.

RuCAM: Comparative Argumentative Machine for the Russian Language.
Maslova, M., Rebrikov, S., Artsishevski, A., Zaczek, S., Biemann C., Nikishina I. (2023): In Proceedings of the 11th Intl. Conf. on Analysis of Images, Social Networks and Texts (AIST 2023), Yerevan, Armenia. LNCS, vol. 14486. Springer. (publishing in process).

Leveraging Taxonomic information from Large Language Models for Hyponymy Prediction.
Chernomorchenko, P., Panchenko, A., Nikishina I. (2023): In Proc. of the 11th Intl. Conf. on Analysis of Images, Social Networks and Texts (AIST 2023), Yerevan, Armenia. LNCS, vol. 14486. Springer. (publishing in process).

Large Language Models Meet Knowledge Graphs to Answer Factoid Questions.
Salnikov, M., Le, H., Rajput, P., Nikishina, I., Braslavski, P., Malykh, V., Panchenko, A. In The 37th Pacific Asia Conference on Language, Information and Computation, Hong Kong, China, 2023.

Do Not Marginalize Mechanisms, Rather Consolidate!.
Willig, M., Zečević, M., Dhami, D. S., & Kersting, K. In Thirty-seventh Conference on Neural Information Processing Systems. (2023, November).

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal.
Zečević, M., Willig, M., Dhami, D. S., & Kersting, K. In Transactions on Machine Learning Research. 2023.

Indicative Summarization of Long Discussions.
Syed, S., Schwabe, D., Al-Khatib, K. and Potthast, M. In the 2023 Conf. on Empirical Methods in Natural Language Processing (EMNLP), pages 2752–2788. Association for Computational Linguistics. (December 2023).

A New Dataset for Causality Identification in Argumentative Texts.
Al-Khatib, K., Völske, M., Syed, S., Le, A., Potthast, M. and Stein, B. In the 24th Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 349–354. Association for Computational Linguistics. (September 2023).

Frame-oriented Summarization of Argumentative Discussions.
Syed, S., Ziegenbein, T., Heinisch, P., Wachsmuth, H. and Potthast, M. In the 24th Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 114–129. Association for Computational Linguistics. (September 2023).

Modeling Appropriate Language in Argumentation.
Ziegenbein, T., Syed,A., Lange, F., Potthast, M. and Wachsmuth, H. In Proc. of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pages 4344–4363, Association for Computational Linguistics. (July 2023).

Building an atlas of knowledge for invasion biology and beyond!
Bernard-Verdier, M. et al. In 2nd enKORE-INAS Workshop. Research Ideas and Outcomes, 9. (2023).

Summary report of the 1st enKORE-INAS workshop.
Bernard-Verdier, M., Heger, T., Jeschke, J. M., Mietchen, D., & Musseau, C. (2023).

Ecological Restoration in Support of Sustainability Transitions: Repairing the Planet in the Anthropocene.
Cooke, S. J. et al. In: M. U. Hensel, D. Sunguroğlu Hensel, C. R. Binder, & F. Ludwig (Eds.), Introduction to Designing Environments: Paradigms & Approaches, Springer International Publishing: 93-112. (2023).

Hypotheses in urban ecology: building a common knowledge base.
Lokatis, S. et al. In Biological Reviews. (2023).

A synthesis of biological invasion hypotheses associated with the introduction–naturalisation–invasion continuum.
Daly, E. Z., et al. In: Oikos, 2023, e09645.

AQUAPLANE: The Argument Quality Explainer App.
Britner, S., Dumani, L. and Schenkel, R. In: Proc. of the 32nd ACM Intl. Conf. on Information and Knowledge Management (CIKM 2023), pages 5015–5020, 2023.

Case-Based Adaptation of Argument Graphs with WordNet and Large Language Models
Lenz, M. and Bergmann, R. In: Case-Based Reasoning Research and Development, pages 263–278, 2023.

Trust Me, I Am an Expert: Predicting the Credibility of Experts for Statements.
Nilles, M., Dumani, L., Metzler, B. and Schenkel, R. In: Proc. of the Workshops at the 31st Intl. Conf. on Case-Based Reasoning (ICCBR-WS 2023), pages 114–128, CEUR Workshop Proceedings. Aberdeen, Scotland, 2023.

Argument-Mining from Podcasts Using ChatGPT.
Pojoni, ML, Dumani, L. and Schenkel, R. In: Proc. of the Workshops at the 31st Intl. Conf. on Case-Based Reasoning (ICCBR-WS 2023), pages 129–144, CEUR Workshop Proceedings. Aberdeen, Scotland, 2023.

CLEARNESS: Coreference Resolution for Generating and Ranking Arguments Extracted from Debate Portals for Queries.
Weidmann, J., Dumani, L., and Schenkel, R. In: Lernen, Wissen, Daten, Analysen (LWDA), CEUR Workshop Proceedings, pages 161–174, Marburg, Germany, 2023.

ACCEPT at SemEval-2023 Task 3: An Ensemble-based Approach to Multilingual Framing Detection.
Philipp Heinisch, Moritz Plenz, Anette Frank, and Philipp Cimiano In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1347–1358, Toronto, Canada. Association for Computational Linguistics, 2023

Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks.
Moritz Plenz, Juri Opitz, Philipp Heinisch, Philipp Cimiano, and Anette Frank In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6130–6158, Toronto, Canada. Association for Computational Linguistics, 2023

2022

Improving Neural Political Statement Classification with Class Hierarchical Information.
Dayanik, E., Blessing, A., Blokker, N., Haunss, S., Kuhn, J., Lapesa, G. and Pado, S. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2367–2382, Dublin, Ireland. Association for Computational Linguistics, 2022.

Optimizing text representations to capture (dis)similarity between political parties.
Ceron, T., Blokker, N. and Padó, S. In Proc. of the 26th Conf. on Computational Natural Language Learning (CoNLL), pages 325–338, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics, 2022.

Boundary Detection and Categorization of Argument Aspects via Supervised Learning.
Ruckdeschel, M. and Wiedemann, G. In Proc. of the 9th Workshop on Argument Mining (pp. 126-136). (2022, October).

Few-shot learning for argument aspects of the nuclear energy debate.
Jurkschat, L., Wiedemann, G., Heinrich, M., Ruckdeschel, M., and Torge, S. In Proc. of the 13th Language Resources and Evaluation Conf. (pp. 663-672). (2022, June).

Why Justifications of Claims Matter for Understanding Party Positions.
Blokker, N., Ceron, T., Blessing, A., Dayanık, E., Haunss, S., Kuhn, J., Lapesa. G. and Padó, S. In Proc. of the 2nd Workshop on Computational Linguistics for Political Text Analysis, 2022.

Argumentation Schemes for Blockchain Deanonymization.
Deuber, D., Gruber, J., Humml, M., Ronge, V., and Scheler, N. In Proc. of the 16th Intl. Workshop on Juris-informatics (JURISIN 2022). Kyoto Intl. Conf. Center, Kyoto, Japan, 2022.

Webis at TREC 2022: Deep Learning and Health Misinformation,
Bondarenko, A. et al. In: Proced. of the 31th Interational Text Retrieval Conference (TREC 2022), November, 2022.

CausalQA: A Benchmark for Causal Question Answering.
Bondarenko, A. et al. In Proc. of the 29th International Conf. on Computational Linguistics (COLING 2022), pages 3296–3308, Gyeongju, Republic of Korea. International Committee on Computational Linguistics, 2022.

The Alternating-Time μ-Calculus with Disjunctive Explicit Strategies.
Göttlinger, M., Schröder, L., and Pattinson, D. In Christel Baier and Jean Goubault-Larrecq (Eds.), Leibniz International Proc. in Informatics (LIPIcs) (pp. 26:1-26:22). University of Ljubljana, SI: Dagstuhl, Germany: Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2021.

TaxFree: a Visualization Tool for Candidate-free Taxonomy Enrichment.
Nikishina, I., Adrianov, I., Vakhitova, A., Panchenko, A. In: The 2nd Conf. of the Asia- Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, 2022.

Can Foundation Models Talk Causality?. In UAI 2022 Workshop on Causal Representation Learning.
Willig, M., Zečević, M., Dhami, D. S., & Kersting, K. (2022, July).

Conditional sum-product networks: Modular probabilistic circuits via gate functions.
Shao, X., Molina, A., Vergari, A., Stelzner, K., Peharz, R., Liebig, T., & Kersting, K. In International Journal of Approximate Reasoning, 140, 298-313, 2022.

Linking a Hypothesis Network From the Domain of Invasion Biology to a Corpus of Scientific Abstracts: The INAS Dataset.
Brinner, M., Heger, T., and Zarriess, S. In Proc. of the first Workshop on Information Extraction from Scientific Publications (pp. 32-42). Association for Computational Linguistics. (2022).

Mapping and assessing the knowledge base of ecological restoration.
Heger, T., et al. In: Restoration Ecology. Blogpost with summary (2022).

User-Centric Argument Mining with ArgueMapper and Arguebuf.
Lenz, M. and Bergmann, R. In: Computational Models of Argument, Vol. 353, Frontiers in Artificial Intelligence and Applications, Cardiff, Wales, 2022.

QualiAssistant: Extracting Qualia Structures from Texts.
Biertz, M., Dumani, L., Nilles, M., Metzler, B. and Schenkel, R. In Proc. of the 9th Workshop on Argument Mining, pages 199–208. Online and in Gyeongju, Republic of Korea: Intl. Conf. on Computational Linguistics, 2022.

Workshop on Text Mining and Generation (TMG): Preface.
Lenz, M., Dumani, L., Bondarenko, A. and Syed, S. In: Joint Proceedings of Workshops, Tutorials and Doctoral Consortium Co-Located with the 45rd German Conference on Artificial Intelligence (KI 2022), CEUR Workshop Proceedings, Virtual Event, Trier, 2022.

Comparing Unsupervised Algorithms to Construct Argument Graphs.
Lenz, M., Dumani, L. and Sahitaj, P. In: Joint Proceedings of Workshops, Tutorials and Doctoral Consortium Co-Located with the 45rd German Conference on Artificial Intelligence (KI 2022), CEUR Workshop Proceedings, Virtual Event, Trier, 2022.

On Selecting Training Corpora for Cross-Domain Claim Detection.
Schaefer, R., Knaebel, R. and Stede, M. In Proc. of the 9th Workshop on Argument Mining, pages 181–186, Online and in Gyeongju, Republic of Korea. Intl. Conf. on Computational Linguistics, 2022.

Towards Identifying Alternative-Lexicalization Signals of Discourse Relations.
Knaebel, R. and Stede, M. In Proc. of the 29th Intl. Conf. on Computational Linguistics, pages 837–850, Gyeongju, Republic of Korea. International Committee on Computational Linguistics, 2022.

Overview of the 2022 Validity and Novelty Prediction Shared Task.
Heinisch, P., Frank, A., Opitz, J., Plenz, M., and Cimiano, P. In Proc. of the 9th Workshop on Argument Mining, pages 84–94, Online and in Gyeongju, Republic of Korea. Intl. Conf. on Computational Linguistics, 2022.

Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions.
Heinisch, P., Plenz, M., Opitz, J., Frank, A. and Cimiano, P.. In Proc. of the 9th Workshop on Argument Mining, pages 19–33, Online and in Gyeongju, Republic of Korea. Intl. Conf. on Computational Linguistics, 2022.

What are ecological mechanisms? Suggestions for a fine-grained description of causal mechanisms in invasion ecology.
Heger, T. In Biology & Philosophy 37:9. (2022)

INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology.
Heger, T., Zarrieß, S., Algergawy, A., Jeschke, J.M. & König-Ries, B. (2022).  Research Ideas and Outcomes, 8, e80457.

The Invasion Biology Ontology (INBIO) [Data set].
Algergawy, A., Gänßinger, M., Heger, T., Jeschke, J., & König-Ries, B. (2022).

Overview of Touché 2022: Argument Retrieval.
Bondarenko, A., Fröbe, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, Ch., Stein, B., Wachsmuth, H., Potthast, M., and Hagen, M. In the Experimental IR Meets Multilinguality, Multimodality, and Interaction. 13th Intl. Conf. of the CLEF Association (CLEF 2022).

Grimjack at Touché 2022: Axiomatic Re-ranking and Query Reformulation.
Reimer, JH., Huck, J., and Bondarenko, A. In Working Notes Papers of the CLEF 2022 Evaluation Labs.

Identifying Argumentative Questions in Web Search Logs.
Ajjour, Y., Braslavski, P., Bondarenko, A., and Stein, B. In the 45th Intl. ACM Conf. on Research and Development in Information Retrieval (SIGIR 2022).

Axiomatic Retrieval Experimentation with ir_axioms.
Bondarenko, A., Fröbe, M., Reimer, JH, Stein, B., Völske, M., Hagen, M. In the 45th Intl. ACM Conf. on Research and Development in Information Retrieval (SIGIR 2022).

A User Study on Clarifying Comparative Questions.
Bondarenko, A., Shirshakova, E., and Hagen, M. In the Conf. on Human Information Interaction & Retrieval 2022 (CHIIR 2022).

Towards Understanding and Answering Comparative Questions.
Bondarenko, A., Ajjour, Y., Dittmar, V., Homann, N., Braslavski. P. and Hagen, M. In the 15th ACM Intl. Conf. on Web Search and Data Mining (WSDM 2022).

Decomposing Sentence Embeddings into Explainable AMR Meaning Features.
Juri Opitz and Anette Frank. To appear in AACL-IJCNLP 2022, (online).

Strategies for Framing Argumentative Conclusion Generation.
Heinisch, P., Frank, A., Opitz, J., and Cimiano , P. In Proc. of the 15th Intl. Natural Language Generation Conf., Association for Computational Linguistics, (to appear), 2022.

A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation – through the Lens of Semantic Similarity Rating.
Zeidler, L., Opitz, J., and Frank, A. In Proc. of the 11th Joint Conf. on Lexical and Computational Semantics (*SEM), pp. 157--172, Association for Computational Linguistics.

Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions.
Beloucif M., Yimam S.M., Stahlhacke S. and  Biemann C. In the 2022 Intl. Conf. on Language Resources and Evaluation (LREC 2022), Marseille, France, 2022.

Synthesizing evidence from clinical trials with dynamic interactive argument trees.
Sanchez-Graillet O., Witte C., Grimm F., et al. In J Biomed. Semantics 13, 16 (2022).

An annotated corpus of clinical trial publications supporting schema-based relational information extraction.
Sanchez-Graillet O., Witte C., Grimm F., et al. In J Biomed. Semantics 13, 14 (2022).

Intra-Template Entity Compatibility based Slot-Filling for Clinical Trial Information Extraction.
Witte C. and Cimiano P.  In Proc. of the 21st Workshop on Biomedical Language Processing. 2022.

2021

On Cycles, Attackers and Supporters - A Contribution to The Investigation of Dynamics in Abstract Argumentation.
Baumann, R., Ulbricht, M. In IJCAI 2021, 1780-1786. (2021).

Comparing Weak Admissibility Semantics to their Dung-style Counterparts (Extended Abstract).
Baumann, R., Brewka, G., Ulbricht, M. In IJCAI 2021: 4740-4744 (2021).

The Alternating-Time μ-Calculus with Disjunctive Explicit Strategies.
Göttlinger, M., Schröder, L., and Pattinson, D. In Christel Baier and Jean Goubault-Larrecq (Eds.), Leibniz International Proc. in Informatics (LIPIcs) (pp. 26:1-26:22). University of Ljubljana, SI: Dagstuhl, Germany: Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2021.

Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification.
Dayanik, E., Blessing, A., Blokker, N., Haunss, S., Kuhn, J., Lapesa, G. and Padó, S. In Proc. of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021), pages 53–60, Online. Association for Computational Linguistics, 2021.

On classifying whether two texts are on the same side of an argument.
Körner, E., Wiedemann, G., Hakimi, A. D., Heyer, G., and Potthast, M. In Proc. of the 2021 conf. on empirical methods in natural language processing (pp. 10130-10138). (2021, November).

Fine and Coarse Granular Argument Classification before Clustering.
Dumani, L., Wiesenfeldt, T. and Schenkel, R. In: Proc. of the 30th ACM Intl. Conf. on Information & Knowledge Management (CIKM 2021), pages 422–432, Association for Computing Machinery, 2021.

QuARk: A GUI for Quality-Aware Ranking of Arguments.
Nilles, M., Dumani, L. and Schenkel, R. In: Proc. of the 44th Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR 2021), pages 2546–2549, 2021.

The ReCAP Corpus: A Corpus of Complex Argument Graphs on German Education Politics.
Dumani, L. et al. In: IEEE Proc. of the 15th Intl. Conf. on Semantic Computing (ICSC), pages 248–255, 2021.

Argument parsing via corpus queries.
Dykes, Natalie; Evert, Stefan; Göttlinger, Merlin; Heinrich, Philipp; Schröder, Lutz. it - Information Technology 63(1): 31–44, 2021.

Misbeliefs and Biases in Health-Related Searches.
Bondarenko, A., Shirshakova, E., Driker, M., Hagen, M. and Braslavski, P. In  the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021).

Webis at TREC 2021: Deep Learning, Health Misinformation, and Podcasts Tracks.
Bondarenko, A., Fröbe, M., Gohsen, M. Günther, S., Kiesel, J., Schwerter, J., Syed, S., Völske, M., Potthast, M., Stein, B. and Hagen, M. In the 30th International Text Retrieval Conference (TREC 2021).

Probing Pre-trained Language Models for Semantic Attributes and their Values.
Meriem Beloucif and Chris Biemann. In Findings of the Association for Computational Linguistics: EMNLP 2021.

Towards Axiomatic Explanations for Neural Ranking Models.
Völske, M., Bondarenko, A., Fröbe, M., Stein, B., Singh, J., Hagen, M. and Anand, A. In the 2021 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR '21).

Generating Informative Conclusions for Argumentative Texts.
Syed, S., Al-Khatib, K., Alshomary, M., Wachsmuth, H., and Potthast, M. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Association for Computational Linguistics, pp. 3482--3493, 2021.

Summary Explorer: Visualizing the State of the Art in Text Summarization.
Syed, S., Yousef, T., Al-Khatib, K., Jänicke, S., and Potthast, M. In Proc. of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, pp. 185-194, 2021.

Assessing the Sufficiency of Arguments through Conclusion Generation.
Gurcke, T., Alshomary, M.  and Wachsmuth, H. In Proc. of the 8th Workshop on Argument Mining, Association for Computational Linguistics, pp. 67-77, 2021.

Key Point Analysis via Contrastive Learning and Extractive Argument Summarization.
Alshomary, M., Gurcke, T., Syed, S., Heinisch, P., Spliethöver, M., Cimiano, P., Potthast, M. and Wachsmuth, H. In Proc. of the 8th Workshop on Argument Mining, Association for Computational Linguistics, pp. 184-189, 2021.

Knowledge Graphs.
Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., De Melo, G., et al. 2021. ACM Comput. Surv. 54(4): 71:1-71:37 (2021).

Knowledge Graphs.
Hogan, A., Blomqvist, E., Cochez, M., D'Amato, C., De Melo, G., et al. In Synthesis Lectures on Data, Semantics, and Knowledge. Morgan & Claypool Publishers, pp. 1-257, 2021.

Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History.
Schmelzeisen, L., Dima, C., and Staab, S. In Proc. of the 2nd Wikidata Workshop (Wikidata 2021) co-located with the 20th Intl. Semantic Web Conf. (ISWC 2021), Virtual Conference, October 24, 2021.

Opinion building based on the argumentative dialogue system BEA.
Aicher, A., Rach, N., Minker, W. and Ultes, S. In Increasing Naturalness and Flexibility in Spoken Dialogue Interaction, pp. 307-318. Springer, Singapore. 2021.

Determination of Reflective User Engagement in Argumentative Dialogue Systems.
Aicher, A., Minker, W., and Ultes, S. In Proc. of the 21st Workshop on Computational Models of Natural Argument CMNA’21, pp. 1-8, 2021.

AdapterFusion: Non-Destructive Task Composition for Transfer Learning.
Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., and Gurevych, I. In Proc. of the 16th Conf. of the European Chapter of the Association for Computational Linguistics: Main Volume, 487–503. (2021).

Aspect-Controlled Neural Argument Generation.
Schiller, B., Daxenberger, J., and Gurevych, I. In the Annual Conf. of the North American Chapter of the Association for Computational Linguistics, 380–396. (2021).

Stance Detection Benchmark: How Robust Is Your Stance Detection?
Schiller, B., Daxenberger, J., and Gurevych, I. In KI - Künstliche Intelligenz (Issue Preprint). Springer. (2021).

COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet.
Becker, M., Korfhage, K., and Frank, A. In Proc. of the Software Demonstrations of the 16th Conf. of the European Chapter of the Association for Computational Linguistics (EACL 2021), pp. 119--126. 2021.

Reconstructing Implicit Knowledge with Language Models.
Becker, M., Liang, S., and Frank, A. In Proc. of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures at NAACL 2021, pp. 11--24, *Best Paper Award*.

CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts.
Becker, M., Korfhage, K., Paul, D., and Frank, A. In Proc. of the 14th Intl. Workshop on Computational Semantics (IWCS), Groningen, The Netherlands (Online), pp. 21--32, Association for Computational Linguistics. 2021.

Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity.
Opitz, J., Daza, A., and Frank, A. In Transactions of the Association for Computational Linguistics (TACL), 9, 1425--1441. 2021.

Explainable Unsupervised Argument Similarity Rating with Abstract Meaning Representation and Conclusion Generation.
Opitz, J., Heinisch, P., Wiesenbach, P., Cimiano, P., and Frank, A. In Proc. of the Eighth Argument Mining Workshop, pp. 24--35, Association for Computational Linguistics, *Best Paper Award*.

Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR.
Opitz, J. and Frank, A. In Proc. of the 16th Conf. of the European Chapter of the Association for Computational Linguistics (EACL 2021), Online, pp. 1504--1518, 2021.

Overview of Touché 2021: Argument Retrieval.
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., Hagen, M. In Proc. of the Intl. Conf. of the Cross-Language Evaluation Forum for European Languages CLEF 2021: Experimental IR Meets Multilinguality, Multimodality, and Interaction, Virtual Event, 2021.

How can BERT Understand High-level Semantics?
Beloucif, M., Biemann, C. In NSNLI Workshop at IJCAI 2021, Online, 2021.

Is Python better than MATLAB for Deep Learning? Answering Comparative Questions in Natural Language.
Chekalina, V., Bondarenko, A., Biemann, C., Beloucif, M., Logacheva, V., Panchenko, A. In the 2021 Conf. of the European Chapter of the Association for Computational Linguistics - System Demonstrations. Kyiv, Ukraine (Online), 2021.

Overview of Touché 2021: Argument Retrieval. Extended Abstract.
Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour,Y., Panchenko, A., Biemann,C., Stein, B., Wachsmuth, H., Potthast, M., Hagen, M. In Proc. of ECIR 2021, Lucca, Italy (Online), 2021.

Ctro-Editor: A web-based tool to capture clinical trial data for aggregation and pooling.
Sanchez-Graillet O, Kramer-Sunderbrink A, Cimiano P. In K-CAP '21. New York: Association for Computing Machinery; 2021

2020

Timed Abstract Dialectical Frameworks.
Baumann, R., Heinrich, M. In COMMA 2020, 103-110 (2020).

A Python Script for Abstract Dialectical Frameworks.
Baumann, R., Heinrich, M. In SAFA 2020, 74-79. (2020).

Comparing Weak Admissibility Semantics to their Dung-style Counterparts - Reduct, Modularization, and Strong Equivalence in Abstract Argumentation.
Baumann, R., Brewka, G., Ulbricht, M. In Proc. of the 17th Intl. Conf. on Principles of Knowledge Representation and Reasoning (KR 2020), 79-88. (2020).

Revisiting the Foundations of Abstract Argumentation - Semantics Based on Weak Admissibility and Weak Defense.
Baumann, R., Brewka, G., Ulbricht, M. In AAAI 2020, 2742-2749. (2020).

Swimming with the Tide? Positional Claim Detection across Political Text Types.
Blokker, N., Dayanik, E., Lapesa, G. and Padó, S. In Proc. of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 24–34, Online. Association for Computational Linguistics, 2020.

The road map to FAME: A framework for mining and formal evaluation of arguments.
Baumann, R., Wiedemann, G., Heinrich, M., Hakimi, A. D., and Heyer, G. Datenbank-Spektrum, 20, 107-113, 2020.

Quality-Aware Ranking of Arguments.
Dumani, L. and Schenkel, R. In: Proceedings of the 29th ACM Intl. Conf. on Information & Knowledge Management (CIKM 2020), pages 335–344, Virtual Event, Ireland: Association for Computing Machinery, 2020.

Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs.
Lenz, M. et al. In: Proc. of the 8th Intl. Conf. on Computational Models of Argument, pages 263–270, Computational Models of Argument. Frontiers in Artificial Intelligence and Applications, Perugia, Italy: IOS Press, 2020.

The ReCAP Project.
Bergmann, R. et al. In: Datenbank-Spektrum 20, pages 93–98, 2020.

Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model.
Ollinger, S., Dumani, L., Sahitaj, P., Bergmann, R. and Schenkel, R. In arXiv, 2020.

Segmenting and Clustering Noisy Arguments
Dumani, L., Kreutz, CK, Biertz, M., Witry, A. and Schenkel, R. In: Proc, of the Conference ”Lernen, Wissen, Daten, Analysen”, CEUR Workshop Proceedings, pages 23–34, Online, 2020.

Reconstructing Arguments from Noisy Text: Introduction to the RANT project.
Dykes, Natalie; Evert, Stefan; Göttlinger, Merlin; Heinrich, Philipp; Schröder, Lutz. Datenbank-Spektrum 20: 123–129, 2020.

Webis at TREC 2020: Health Misinformation Track.
Bevendorff, J., Bondarenko, A., Fröbe, M., Günther, S., Völske, M., Stein, B. and Hagen, M. In the 29th International Text Retrieval Conference (TREC 2020).

Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers.
Lauscher, A., Majewska, O., Ribeiro, L. F. R., Gurevych, I., Rozanov, N., and Glavaš, G. In Proc. of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 43–49. (2020).

Social Commonsense Reasoning with Multi-Head Knowledge Attention.
Paul, D. and Frank, A. In Findings of the 2020 Conf. on Empirical Methods in Natural Language Processing (EMNLP), pp. 2969--2980, Association for Computational Linguistics, 2020.

Overview of Touché 2020: Argument Retrieval.
Bondarenko, A., Fröbe, M., Beloucif, M., Gienapp, L., Ajjour, Y., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M. Hagen, M. In Working Notes of CLEF 2020 - Conf. and Labs of the Evaluation Forum. Thessaloniki, Greece, 2020.

Answering Comparative Questions with Arguments.
Bondarenko, A., Panchenko, A., Beloucif, M. Biemann, C., Hagen, M. Datenbank Spektrum 20:155–160, 2020.

Word Sense Disambiguation for 158 Languages using Word Embeddings Only.
Logacheva, V., Teslenko, D., Shelmanov, A., Remus, S., Ustalov, D., Kutuzov, A., Artemova, E., Biemann, C., Ponzetto, S.P., Panchenko, A. Proc. of the Intl. Conf. on Language Resources and Evaluation (LREC 2020), Marseille, France, 2020.

Fine-Grained Argument Unit Recognition and Classification.
Trautmann D., Daxenberger J. Stab C., Schütze H., Gurevych I., In Proc. of the Thirty-Fourth AAAI Conf. on Artificial Intelligence (AAAI'20), 2020.

A Framework for Argument Retrieval - Ranking Argument Clusters by Frequency and Specificity.
Dumani, L., Neumann, P.J., Schenkel, R. In ECIR 2020: Advances in Information Retrieval - 42nd European Conf. on IR Research.

Predicting persuasive effectiveness for multimodal behavior adaptation using bipolar weighted argument graphs.
Weber, K., Janowski, K., Rach, N., Weitz, K., Minker, W., Ultes, S. and André, E. In Proc. of the 19th Intl. Conf. on Autonomous Agents and Multi-Agent Systems (AAMAS '20). ACM, New York, USA. Nominated for the Best Paper Award.

Towards Demystifying Subliminal Persuasiveness: Using XAI-Techniques to Highlight Persuasive Markers of Public Speeches.
Weber, K., Tinnes, L., Huber, T., Heimerl, A., Pohlen, E., Reinecker, M-L. and André, E. In the 2nd Intl. Workshop on EXplainable, TRansparent Autonomous Agents and Multi-Agent Systems (EXTAAMAS '20). Springer. (In press).

Evaluation of Argument Search Approaches in the Context of Argumentative Dialogue Systems.
Rach, N., Matsuda, Y., Daxenberger, J., Ultes, S., Yasumoto, K. and Minker, W. In Proc. of the 12th Intl. Conf. on Language Resources and Evaluation (LREC), Marseille, France, 2020.

Epistemic Graphs for Representing and Reasoning with Positive and Negative Influences of Arguments.
Hunter, A., Polberg, S. and Thimm, M. In Artificial intelligence 281:103236, 2020.

Touché: First Shared Task on Argument Retrieval.
Bondarenko, A., Hagen, M., Potthast, M., Wachsmuth, H., Beloucif, M., Biemann, Ch., Panchenko, A., Stein, B. In Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, Vol. 12036. Springer.

Comparative Web Search Questions.
Bondarenko, A., Braslavski, P., Völske, M., Aly, R., Fröbe, M., Panchenko, A., Biemann, Ch., Stein, B. and Hagen, M. In Proc. of the 13th Intl. Conf. on Web Search and Data Mining (WSDM ’20). Association for Computing Machinery, New York, NY, USA, 52–60.

Independent argumentation schemes? Transferring argument queries from Brexit to environment tweets.
Dykes, N., Heinrich, P. and Blombach, A. Presentation at ICAME41, Heidelberg, Germany, 2020.

Implicit Knowledge in Argumentative Texts: An Annotated Corpus.
Becker, M., Korfhage, K., and Frank, A. In Proceedings of the 12th Conf. on Language Resources and Evaluation (LREC). Marseille, France, pp. 2316--2324. 2020.

Integrating Manual and Automatic Annotation for the Creation of Discourse Network Data Sets.
Haunss, S., Kuhn, J., Padó, S., Blessing, A., Blokker, N., Dayanik, E. and Lapesa, G. In Politics and Governance, 8(2), 2020. (To appear).

Masking Actor Information Leads to Fairer Political Claims Detection.
Dayanik, E. and Padó, S. In Proc. of ACL. Seattle, WA, 2020. (To appear).

DEbateNet-mig15: Tracing the 2015 Immigration Debate in Germany Over Time.
Lapesa, G., Blessing, A., Blokker, N., Dayanik, E., Haunss, S., Kuhn, J. and Padó, S. In Proc. of LREC. Marseille, France, 2020. (To appear).

AMR Similarity Metrics from Principles.
Paul, D., Opitz, J., Becker, M., Kobbe, J., Hirst, G., and Frank, A. In Transactions of the Association for Computational Linguistics, 8, 522--538. 2020.

Argumentative Relation Classification with Background Knowledge.
Paul, D., Opitz, J., Becker, M., Kobbe, J., Hirst, G. and Frank, A. In Proc. of the 8th Intl. Conf. on Computational Models of Argument (COMMA 2020), vol. 326 of Frontiers in Artificial Intelligence and Applications, pp. 319--330, Computational Models of Argument, *Best Student Paper Award Nomination* (3 runner-ups).

Explaining Arguments with Background Knowledge. Towards Knowledge-based Argumentation Analysis.
Becker, M., Hulpus, I., Paul, D., Opitz, J., Kobbe, J., Stuckenschmidt, H. and Frank, A. (2020). In Datenbank Spektrum 20:131–141, Special Issue: Argumentation Intelligence, 20, 131--141..

Unsupervised Stance Detection for Arguments from Consequences.
Kobbe, J., Hulpus, I. and Stuckenschmidt, H. (2020). In Proc. of the 2020 Conf. on Empirical Methods in Natural Language Processing (EMNLP)

2019

If Nothing Is Accepted - Repairing Argumentation Frameworks.
Baumann, R., Ulbricht, M. In J. Artif. Intell. Res. 66, 1099-1145 (2019).

A Systematic Comparison of Methods for Finding Good Premises for Claims. 
Dumani, L.; Schenkel, R. In SIGIR 2019: Proc. of the 42nd Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval.

Good Premises Retrieval via a Two-Stage Argument Retrieval Model. 
Dumani, L. In GvDB 2019: Proc. of the 31st GI-Workshop Grundlagen von Datenbanken, CEUR Workshop Proc. Vol. 2367.

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections. 
El-Assday, M., Kehlbeck, R., Collins, CH., Keim, D.A., Deussen, O. (2019). IEEE Transactions on Visualization and Computer Graphics, 2019.

Aspectual Reasoning in LFG – A Computational Approach to Grammatical and Lexical Aspect. 
Zymla, M. and Butt, M. In Proc. of the LFG’19 Conf. 2019.

On the Syntax/Semantics Interface in Computational Glue Semantics: A Case Study. 
Zymla, M., Sigwarth, G., Butt, M. In Proc. of the LFG’19 Conf. 2019.

VIANA: Visual Interactive Annotation of Argumentation.
Sperrle, F., Sevastjanova, R., Kehlbeck, R., El-Assady, M. (2019) IEEE Conf. on Visual Analytics Science and Technology (VAST). 2019.

Human Trust Modeling for Bias Mitigation in Artificial Intelligence.
Sperrle, F., Schlegel, U., Keim, D.A., El-Assady. M. ACM CHI 2019 Workshop: Where is the Human? Bridging the Gap Between AI and HCI, 2019.

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution.
El-Assady, M., Sperrle, F., Deussen, O., Keim, D., Collins, Ch. (2019) IEEE Transactions on Visualization and Computer Graphics, 2019.

Abstract Graphs and Abstract Paths for Knowledge Graph Completion.
Nastase, V. and Kotnis, B. In Proc. of the 8th Joint Conf. on Lexical and Computational Semantics (* SEM 2019), Minneapolis, Minnesota, 2019.

Dissecting Content and Context in Argumentative Relation Analysis.
Opitz, J. and Frank, A. In Proc. of the 6th Workshop on Argument Mining, Florence, Italy, 2019.

Automatic Accuracy Prediction for AMR Parsing. 
Opitz, J. and Frank, A. In Proc. of 8th Joint Conf. on Lexical and Computational Semantics (*SEM), Minneapolis, Minnesota, 2019.

Argumentative Relation Classification as Plausibility Ranking. 
Opitz, J. In Proc. of the 15th Conf. on Natural Language Processing (KONVENS), 2019.

A Spreading Activation Framework for Tracking Conceptual Complexity of Texts.
Hulpus, I., Stajner, S. and Stuckenschmidt, H. In Proc. of the 57th Conf. of the Association for Computational Linguistics (ACL 2019), pp. 3878-3887. 2019.

Anytime bottom-up rule learning for knowledge graph completion.
Meilicke, C., Chekol, M.W., Ruffinelli, D. and Stuckenschmidt, H. In Proc. of the 28h Intl. Joint Conf. on Artificial Intelligence (IJCAI-2019).

Towards Explaining Natural Language Arguments with Background Knowledge.
Hulpus, I., Kobbe, J., Meilicke, C., Stuckenschmidt, H., Hirst, G., Becker, M., Opitz, J., Nastase, V. and Frank, A. In the Workshop on Semantic Explainability (SemEx 2019) in the 18th Intl. Semantic Web Conf. (ISWC 2019), Christchurch, New Zealand, 2019.

Clustering of Argument Graphs Using Semantic Similarity Measures.
Block, K., Trumm, S., Sahitaj, P., Ollinger, S. and Bergmann, R. In KI 2019: Advances in Artificial Intelligence: 42nd German Conf. on AI, Kassel, Germany, September 23–26, 2019, Proceedings, 2019.Springer.

Semantic Textual Similarity Measures for Case-Based Retrieval of Argument Graphs.
Lenz, M.; Ollinger, S.; Sahitaj, P.; and Bergmann, R. In Proceedings of the 27th Intl. Conf. on Case-Based Reasoning Research and Development (ICCBR 2019), pp. 219–234, Otzenhausen, Germany, 2019.

CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors.
Baris, I.,Schmelzeisen, L. and Staab, S. In the 3th Intl. Workshop on Semantic Evaluation, Minneapolis, USA, 2019.

Categorizing Comparative Sentences.
Panchenko, A., Bondarenko, A., Franzek, M., Hagen, M. and Biemann, Ch. In Proc. of the the 6th Workshop on Argument Mining (ArgMining'2019) in the ACL 2019, Florence, Italy, 2019.

Annotating and analyzing the interactions between meaning relations.
Gold, D., Kovatchev, V., and Zesch, T. In Proceedings of the 13th Linguistic Annotation Workshop in the ACL 2019 (pp. 26--6), Florence, Italy, 2019.

An Environment for Relational Annotation of Political Debates.
Blessing, A., Blokker, N., Haunss, S., Kuhn, J., Lapesa, G. and Padó, S. In Proc. of the 57th Annual Meeting of the ACL: System Demonstrations (pp. 105-110),  Florence, Italy, 2019.

Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates.
Padó, S., Blessing, A., Blokker, N., Dayanik, E., Haunss, S. and Kuhn, J. In Proc. of the 57th Annual Meeting of the ACL (pp. 2841-2847), Florence, Italy, 2019.

Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting.
Becker, M., Staniek, M., Nastase, V., Frank, A. In RELATIONS - Workshop on meaning relations between phrases and sentences (co-located with IWCS), Gothenburg, Sweden, 2019.

Using Topic Specific Features for Argument Stance Recognition.
Eljasik-Swoboda, T., Engel, F. and Hemmje, M. In Proc. of the 8th Intl. Conf. on Data Science, Technology and Applications (DATA), pp. 13-22, Prague, Czech Republic, 2019.

TARGER: NeuralArgument Mining at Your Fingertips.
Chernodub, A., Oliynyk, O., Heidenreich, P., Bondarenko, A., Hagen, M., Biemann, C.and Panchenko, A. In Proc. of the 57th Annual Meeting of the ACL: System Demonstrations (pp. 195-200), Florence, Italy, 2019.

Answering Comparative Questions: Better than Ten-Blue-Links?
Schildwächter, M.,Bondarenko, A., Zenker, J., Hagen, M., Biemann, C. and Panchenko, A. (2019). In Proc. of the 2019 Conf. on Human Information Interaction and Retrieval CHIIR '19, (pp. 361-365). Glasgow, Scotland, UK.

Classification and Clustering of Arguments with Contextualized Word Embeddings.
Reimers, N., Schiller, B., Beck, T., Daxenberger, J., Stab, Ch.  and Gurevych, I. In Proc. of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 567-578. Florence, Italy, July, 2019.

Exploiting Background Knowledge for Argumentative Relation Classification.
Kobbe, J., Opitz, J., Becker, M.,Hulpus, I., Stuckenschmidt, H. and Frank, A. In the 2nd Biennial Conf. on Language, Data and Knowledge LDK 2019, Dagstuhl, Germany, 2019. (Best Student Paper Award).

Similarity Measures for Case-Based Retrieval of Natural Language Argument Graphs in Argumentation Machines.
Bergmann, R., Lenz, M., Ollinger, S., Pfister, M. In Proc. of the 32nd Intl. Conf. of The Florida Artificial Intelligence Research Society (FLAIRS 2019), Sarasota, Florida, USA, 2019.

C-TrO: an Ontology for Summarization and Aggregation of the Level of Evidence in Clinical Trials.
Sanchez-Graillet, O., Cimiano, P., Witte, C and Ell, B. In the Proc. of the Workshop Ontologies and Data in Life Sciences (ODLS 2019) in the Joint Ontology Workshops' (JOWO 2019), Graz, Austria.

Argumentation Schemes for Clinical Interventions. Towards an Evidence-aggregation System for Medical Recommendations.
Sanchez-Graillet, O., Cimiano, P., Witte, C and Ell, B. In The 4th Internt. Conf. HEALTHINFO 2019, November 24-28, Valencia, Spain, 2019.

Emotion recognition based preference modelling in argumentative dialogue systems.
Rach, N., Weber, K., Aicher, A., Lingenfelser, F., André, E. and Minker, W.. In the IEEE Intl. Conf. on Pervasive Computing and Communications Workshops (PerCom Workshops), Kyoto, Japan, 2019.

Opinion Building based on the Argumentative Dialogue System BEA.
Aicher, A., Rach, N., Minker, W. and Ultes, S. In Proc. of the 10th Intl. Workshop on Spoken Dialog Systems Technology (IWSDS 2019), Siracusa, Italy, 2019.

Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study.
Kuhlmann, I. and Thimm, M.  In Proc. of the 13th Intl. Conf. on Scalable Uncertainty Management (SUM'19). December 2019.

Ranked Programming.
Rienstra, T. In Proc. of the 28th Intl. Joint Conf. on Artificial Intelligence (IJCAI'19), August 2019.

A General Approach to Reasoning with Probabilities.
Cerutti, F. and Thimm, M. In Intl. Journal of Approximate Reasoning, 111:35-50. August 2019.

Automatic Bayesian Density Analysis.
Vergari, A. Molina, A., Peharz, R., Ghahramani, Z., Kersting, K., Valera, I. In Proc. of the Thirty-Third AAAI Conf. on Artificial Intelligence (AAAI'19), 2019.

Explanatory Interactive Machine Learning.
Teso, S., Kersting, K. In Proc. of the 2nd AAAI/ACM Conf. on AI, Ethics, and Society (AIES). 2019.

Webis at TREC 2019: Decision Track.
Bondarenko, A., Fröbe, M., Kasturia, V., Völske, M., Stein, B. and Hagen, M. In the Proc. of the 28th Text REtrieval Conference (TREC 2019).

Emerging Named Entity Recognition on Retrieval Features in an Affective Computing Corpus.
Nawroth, C., Engel, F., Mc Kevitt, P. and Hemmje, M. L. In Proc. of the IEEE Intl. Conf. on Bioinformatics and Biomedicine (BIBM 2019), pp. 2860-2868, San Diego, CA, USA, 2019.

Reconstructing Twitter arguments with corpus linguistics.
Dykes, N., Heinrich, P. and Evert, S. Presentation at ICAME40: Language in Time, Time in Language. Neuchâtel, Switzerland, 2019.

Arguing Brexit on Twitter. A corpus linguistic study.
Dykes, N., Heinrich, P. and Evert, S. Presentation at the European Conference on Argumentation 2019, Groningen, Netherlands, 2019.

Classifying Semantic Clause Types with Recurrent Neural Networks: Analysis of Attention, Context and Genre Characteristics.
Becker, M., Staniek, M., Nastase, V., Palmer, A., and Frank, A. In TAL Journal (Traitement Automatique des Langues / Natural Language Processing): Special issue Deep Learning for Natural Language Processing (2019).

2018

Augmenting Public Deliberations through Stream Argument Analytics and Visualisations.
Plüss, B., Sperrle, F., Gold, V., El-Assady, M., Hautli, A., Budzynska, K., Reed, Ch. In  The Leipzig Symposium on Visualization in Applications, 2018.

Speculative Execution for Guided Visual Analytics.
Sperrle, F., Bernard, J., Sedlmair, M., Keim, D., El-Assady. M. In The Workshop on Machine Learning from User Interaction for Visualization and Analytics as part of the IEEE VIS,2018.

Utilizing Argument Mining Techniques for Argumentative Dialogue Systems.
Rach, N., Langhammer, S., Minker, W. and Ultes, S. In Proc. of the 9th Intl. Workshop On Spoken Dialogue Systems (IWSDS), Singapore, 2018.

Handling Unknown User Arguments in Argumentative Dialogue Systems.
Rach, N., Minker, W. and Ultes, S. Presented at the 32nd British Human Computer Interaction Conf. (HCI), Belfast Markov, 2018.

Games for Persuasive Dialogue.
Rach, N., Minker, W. and Ultes, S. Accepted for presentation at the 7th Intl. Conf. on Computational Models of Argument (COMMA), Warsaw, 2018.

EVA: A Multimodal Argumentative Dialogue System.
Rach, N., Weber, K., Pragst, L.,André, E., Minker, W. and Ultes, S. Accepted for presentation at the 20th ACM Intl. Conf. on Multimodal Interaction, Boulder, Colorado, October, 2018.

Probabilistic Abstract Argumentation based on SCC Decomposability.
Rienstra, T., Thimm, M., Liao, B., van der Torre, L. In Proceedings of the 16th Intl. Conf. on Principles of Knowledge Representation and Reasoning (KR'18), October, 2018.

A General Approach to Reasoning with Probabilities (Extended Abstract).
Cerutti, F., Thimm, M. In Proc. of the 16th Intl. Conf. on Principles of Knowledge Representation and Reasoning (KR'18), October, 2018.

Towards Enabling Emerging Named Entity Recognition as a Clinical Information and Argumentation Support.
Nawroth, C., Engel, F.C., Eljasik-Swoboda, T. and Hemmje, M. In Proc. of the 7th Intl. Conf. on Data Science, Technology and Applications (DATA), pp. 47-55, Porto, Portugal, 2018.

Epistemic Attack Semantics.
Thimm, M. Polberg, S., Hunter, A. In Proc. of the Seventh Intl. Conf. on Computational Models of Argumentation (COMMA'18), September, 2018.

Probabilistic Graded Semantics.
Thimm, M., Cerutti, F., Rienstra, T. In Proc. of the Seventh Intl. Conf. on Computational Models of Argumentation (COMMA'18), September, 2018.

Ranking Functions over Labellings.
Rienstra, T. and Thimm, M. In Proc. of the Seventh Intl. Conf. on Computational Models of Argumentation (COMMA'18), September, 2018.

Stochastic Local Search Algorithms for Abstract Argumentation under Stable Semantics.
Thimm, M. In Proc. of the Seventh Intl. Conf. on Computational Models of Argumentation (COMMA'18), September, 2018.

Argumentation as Exogenous Coordination.
van der Torre, L., Rienstra, T., Gabbay, D. In It's All About Coordination. Springer Intl. Publishing, 2018.

Building a Web-Scale Dependency-Parsed Corpus from Common Crawl.
Panchenko, A., Ruppert, E., Faralli, S., Ponzetto, S.P., Biemann, C. In Proc. of LREC 2018, Myazaki, Japan, 2018.

Unsupervised Semantic Frame Induction using Triclustering.
Ustalov D., Panchenko A., Kutuzov A., Biemann C., Ponzetto S. P. In Proc. of the ACL’2018. Melbourne, Australia, 2018.

Unsupervised Sense-Aware Hypernymy Extraction.
Ustalov D., Panchenko A., Biemann C., Ponzetto S.-P. In Proc. of KONVENS’2018. Vienna, Austria. p. 192-201, 2018

Corpus of Aspect-based Sentiment in Political Debates.
Gold, D., Bexte, M. Zesch, T. In Proc. of KONVENS’2018. Vienna, Austria. p. 89-99, 2018.

ReCAP - Information Retrieval and Case-Based Reasoning for Robust Deliberation and Synthesis of Arguments in the Political Discourse.
Bergmann, R., Schenkel, R., Dumani, L., Ollinger, S.In Proc. of the Conf. "Lernen, Wissen, Daten, Analysen", LWDA 2018.

Webis at TREC 2018: Common Core Track.
Bondarenko, A., Völske, M., Panchenko, A., Biemann, Ch., Stein, B., Hagen, M. In the 27th Intl. Text Retrieval Conf. (TREC 2018), November 2018. National Inst. of Standards and Technology (NIST).

Probabilistic Augmentations for Knowledge Representation Formalisms.
Cerutti, F. and Thimm, M. In Proc. of the 2018 Workshop on Hybrid Reasoning and Learning (HRL'18). October 2018.

Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs.
Kotnis, B. and Nastase, V. In KBCOM 2018, Los Angeles, California (2018). 

Using Patterns in Knowledge Graphs for Targeted Information Extraction.
Zhou, M. and Nastase, V. In KBCOM 2018. 

Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs.
Kotnis, B. and Nastase, V. In KBCOM 2018, Los Angeles, California. 

2017

Towards Argumentation-based Classification.
Thimm, M. and Kersting, K. In Logical Foundations of Uncertainty and Machine Learning, Workshop at IJCAI'17. August, 2017.

Project Publications

ReCap Project (Since 2018)