"Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain
H. Wachsmuth, M. Alshomary, in: Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 344–354.

"Page Rank'' for Argument Relevance
H. Wachsmuth, B. Stein, Y. Ajjour, in: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 2017, pp. 1117–1127.

A Computer-assistance Learning System for Emotional Wording
W.-F. Chen, M.-H. Chen, M.-L. Chen, L.-W. Ku, IEEE Transactions on Knowledge and Data Engineering 28 (2015) 1093–1104.

A News Editorial Corpus for Mining Argumentation Strategies
K. Al Khatib, H. Wachsmuth, J. Kiesel, M. Hagen, B. Stein, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp. 3433–3443.

A Plan for Ancillary Copyright: Original Snippets.
M. Potthast, W.-F. Chen, M. Hagen, B. Stein, in: Proceedings of the Second International Workshop on Recent Trends in News Information Retrieval, 2018, pp. 3–5.

A Review Corpus for Argumentation Analysis
H. Wachsmuth, M. Trenkmann, B. Stein, G. Engels, T. Palakarska, in: Proceedings of the 15th International Conference on Intelligent Text Processing and Computational Linguistics, 2014, pp. 115–127.

A Syllable-based Prosody Modeling for L1 and L2 English Speeches
W.-F. Chen, C.-K. Kuo, Y.-R.  Wang, S.-H.  Chen, in: Proceedings of the 8th International Symposium on Chinese Spoken Language Processing, IEEE, 2012, pp. 281–285.

A Universal Model for Discourse-Level Argumentation Analysis
H. Wachsmuth, B. Stein, Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media (2017) 1–24.

A User Study on Snippet Generation: Text Reuse vs. Paraphrases
W.-F. Chen, M. Hagen, B. Stein, M. Potthast, in: Proceedings of the 41st International ACM SIGIR Conference on Research &; Development in Information Retrieval, 2018, pp. 1033–1036.

Abstractive Snippet Generation
W.-F. Chen, S. Syed, B. Stein, M. Hagen, M. Potthast, in: Proceedings of the Web Conference 2020, 2020, pp. 1309–1319.

Analyzing Culture-Specific Argument Structures in Learner Essays
W.-F. Chen, M.-H. Chen, G. Mudgal, H. Wachsmuth, in: Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51–61.

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity
W.-F. Chen, K. Al-Khatib, H. Wachsmuth, B. Stein, in: Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020, pp. 149–154.

Analyzing the Persuasive Effect of Style in News Editorial Argumentation
R. El Baff, H. Wachsmuth, K. Al-Khatib, B. Stein, in: J. Tsujii, J. Hajic (Eds.), Proceedings of 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 553–564.

Application of Sentiment Analysis to Language Learning
M.-H. Chen, W.-F. Chen, L.-W. Ku, IEEE Access 6 (2018) 24433–24442.

Argument from Old Man's View: Assessing Social Bias in Argumentation
M. Spliethöver, H. Wachsmuth, in: Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 76–87.

Argument Novelty and Validity Assessment via Multitask and Transfer Learning
M. Alshomary, M. Stahl, in: Proceedings of the 9th Workshop on Argument Mining, International Conference on Computational Linguistics, Online and in Gyeongju, Republic of Korea, 2022, pp. 111–114.

Argument Search: Assessing Argument Relevance
M. Potthast, L. Gienapp, F. Euchner, N. Heilenkötter, N. Weidmann, H. Wachsmuth, B. Stein, M. Hagen, in: 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019), ACM, 2019, pp. 1117–1120.

Argument Undermining: Counter-Argument Generation by Attacking Weak Premises
M. Alshomary, S. Syed, M. Potthast, H. Wachsmuth, in: Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), Association for Computational Linguistics, 2021, pp. 1816–1827.

Argumentation Quality Assessment: Theory vs. Practice
H. Wachsmuth, N. Naderi, I. Habernal, Y. Hou, G. Hirst, I. Gurevych, B. Stein, in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2017, pp. 250–255.

Argumentation Synthesis following Rhetorical Strategies
H. Wachsmuth, M. Stede, R. El Baff, K. Al Khatib, M. Skeppstedt, B. Stein, in: Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 3753–3765.

Assessing the Sufficiency of Arguments through Conclusion Generation
T. Gurcke, M. Alshomary, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 67–77.

Automatic Pipeline Construction for Real-Time Annotation
H. Wachsmuth, M. Rose, G. Engels, in: A. Gelbukh (Ed.), 14th International Conference on Intelligent Text Processing and Computational Linguistics, 2013, pp. 38–49.

Back to the Roots of Genres: Text Classification by Language Function
H. Wachsmuth, K. Bujna, in: B. Berendt, A. de Vries, W. Fan, C. Macdonald, I. Ounis, I. Ruthven (Eds.), Proceedings of 5th International Joint Conference on Natural Language Processing, 2011, pp. 632–640.

Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning
M. Sengupta, M. Alshomary, H. Wachsmuth, in: Proceedings of the 2022 Workshop on Figurative Language Processing, 2022.

Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation
I. Habernal, H. Wachsmuth, I. Gurevych, B. Stein, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018, pp. 386–396.

Belief-based Generation of Argumentative Claims
M. Alshomary, W.-F. Chen, T. Gurcke, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics, 2021, pp. 224–223.

Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models
M. Spliethöver, H. Wachsmuth, in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, 2021, pp. 552–559.

Book Review: Argumentation Mining
H. Wachsmuth, Computational Linguistics 45 (2019) 603–606.

Building an Argument Search Engine for the Web
H. Wachsmuth, M. Potthast, K. Al-Khatib, Y. Ajjour, J. Puschmann, J. Qu, J. Dorsch, V. Morari, J. Bevendorff, B. Stein, in: Proceedings of the 4th Workshop on Argument Mining, 2017, pp. 49–59.

CauseNet: Towards a Causality Graph Extracted from the Web
S. Heindorf, Y. Scholten, H. Wachsmuth, A.-C. Ngonga Ngomo, M. Potthast, in: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, pp. 3023–3030.

Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus
R. El Baff, H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of the 22nd Conference on Computational Natural Language Learning, Association for Computational Linguistics, 2018, pp. 454–464.

Chinese Textual Sentiment Analysis: Datasets, Resources and Tools
L.-W. Ku, W.-F. Chen, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts, 2016, pp. 5–8.

CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction
G. Skitalinskaya, J. Klaû00, M. Spliethöver, CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event Extraction, Lugano, Switzerland, 2019.

Computational Argumentation Quality Assessment in Natural Language
H. Wachsmuth, N. Naderi, Y. Hou, Y. Bilu, V. Prabhakaran, T.A. Thijm, G. Hirst, B. Stein, in: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 2017, pp. 176–187.

Computational Argumentation Synthesis as a Language Modeling Task
R. El Baff, H. Wachsmuth, K. Al-Khatib, M. Stede, B. Stein, in: Proceedings of the 12th International Conference on Natural Language Generation, Association for Computational Linguistics, 2019, pp. 54–64.

Computational Text Professionalization using Neural Sequence-to-Sequence Models
A. Mishra, Computational Text Professionalization Using Neural Sequence-to-Sequence Models, 2021.

Constructing Efficient Information Extraction Pipelines
H. Wachsmuth, B. Stein, G. Engels, in: B. Berendt, A. de Vries, W. Fan, C. Macdonald, I. Ounis, I. Ruthven (Eds.), 20th ACM International Conference on Information and Knowledge Management, 2011, pp. 2237–2240.

Controlled Neural Sentence-Level Reframing of News Articles
W.-F. Chen, K. Al Khatib, B. Stein, H. Wachsmuth, in: Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 2683–2693.

Cross-Domain Mining of Argumentative Text through Distant Supervision
K. Al-Khatib, H. Wachsmuth, M. Hagen, J. Köhler, B. Stein, in: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016, pp. 1395–1404.

Data Acquisition for Argument Search: The args.me Corpus
Y. Ajjour, H. Wachsmuth, J.  Kiesel, M. Potthast, M. Hagen, B. Stein, in: Proceedings of the 42nd Edition of the German Conference on Artificial Intelligence, 2019, pp. 48–59.

Detecting Media Bias in News Articles using Gaussian Bias Distributions
W.-F. Chen, K. Al-Khatib, B. Stein, H. Wachsmuth, in: Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 4290–4300.

Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation
F.S. Bäumer, W.-F. Chen, M. Geierhos, J. Kersting, H. Wachsmuth, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 65–84.

Domain-aware Text Professionalization using Sequence-to-Sequence Neural Networks
J. Palushi, Domain-Aware Text Professionalization Using Sequence-to-Sequence Neural Networks, 2022.

Efficient Statement Identification for Automatic Market Forecasting
H. Wachsmuth, P. Prettenhofer, B. Stein, in: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), 2010, pp. 1128–1136.

Embarrassed or Awkward? Ranking Emotion Synonyms for ESL Learners’ Appropriate Wording
W.-F. Chen, M. Chen, L.-W. Ku, in: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp. 144–153.

Employing Argumentation Knowledge Graphs for Neural Argument Generation
K. Al-Khatib, L. Trautner, H. Wachsmuth, Y. Hou, B. Stein, in: Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), 2021, pp. 4744–4754.

End-to-End Argumentation Knowledge Graph Construction
K. Al-Khatib, Y. Hou, H. Wachsmuth, C. Jochim, F. Bonin, B. Stein, in: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), 2020, pp. 7367–7374.

Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems
K.J. Rohlfing, P. Cimiano, I. Scharlau, T. Matzner, H.M. Buhl, H. Buschmeier, E. Esposito, A. Grimminger, B. Hammer, R. Haeb-Umbach, I. Horwath, E. Hüllermeier, F. Kern, S. Kopp, K. Thommes, A.-C. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P. Wagner, B. Wrede, IEEE Transactions on Cognitive and Developmental Systems 13 (2021) 717–728.

Extractive Snippet Generation for Arguments
M. Alshomary, N. Düsterhus, H. Wachsmuth, in: Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 1969–1972.

Generating Contrastive Snippets for Argument Search
M. Alshomary, J. Rieskamp, H. Wachsmuth, in: Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21–31.

Generating Informative Conclusions for Argumentative Texts
S. Syed, K. Al-Khatib, M. Alshomary, H. Wachsmuth, M. Potthast, in: Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021): Findings, 2021, pp. 3482–3493.

How to Get Endorsements? Predicting Facebook Likes Using Post Content and User Engagement
W.-F. Chen, Y.-P. Chen, L.-W. Ku, in: International Conference on HCI in Business, Government, and Organizations, 2017, pp. 190–202.

iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing
Z. Nouri, N. Prakash, U. Gadiraju, H. Wachsmuth, in: Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021, 2021.

Identifying Feedback Types to Augment Feedback Comment Generation
M. Stahl, H. Wachsmuth, in: Proceedings of the 16th International Natural Language Generation Conference, n.d.

Identifying the Human Values behind Arguments
J. Kiesel, M. Alshomary, N. Handke, X. Cai, H. Wachsmuth, B. Stein, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4459–4471.

If You Ask Nicely: A Digital Assistant Rebuking Impolite Voice Commands
M. Bonfert, M. Spliethöver, R. Arzaroli, M. Lange, M. Hanci, R. Porzel, in: Proceedings of the 20th ACM International Conference on Multimodal Interaction, 2018.

Information Extraction as a Filtering Task
H. Wachsmuth, B. Stein, G. Engels, in: Proceedings of the 22nd ACM International Conference on Conference on Information &; Knowledge Management, 2013, pp. 2049–2058.

Intrinsic Quality Assessment of Arguments
H. Wachsmuth, T. Werner, in: Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics, 2020, pp. 6739–6745.

Investigating Expectations for Voice-based and Conversational Argument Search on the Web
J. Kiesel, K. Lang, H. Wachsmuth, E. Hornecker, B. Stein, in: Proceedings of the 2020 ACM SIGIR Conference on Human Information Interaction &; Retrieval (CHIIR 2020), 2020, pp. 53–62.

Investigating the argumentation structures of EFL learners from diverse language backgrounds
M.-H. Chen, G. Mudgal, W.-F. Chen, H. Wachsmuth, in: EUROCALL, 2022.

Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation
M. Spliethöver, J. Klaff, H. Heuer, in: Proceedings of the 6th Workshop on Argument Mining, Association for Computational Linguistics, Florence, Italy, 2019, pp. 74–82.

iSoNTRE: The Social Network Transformer into Recommendation Engine
C. Abu Quba Rana, S. Hassas, F. Usama, M. Alshomary, C. Gertosio, 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA) (2014) 169–175.

Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano, M. Potthast, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 184–189.

Knowledge Base Enhanced & User-centric Dialogue Design for OTF Computing
M. Ahmed, Knowledge Base Enhanced &; User-Centric Dialogue Design for OTF Computing, 2022.

Learning Efficient Information Extraction on Heterogeneous Texts
H. Wachsmuth, B. Stein, G. Engels, in: A. Gelbukh (Ed.), Proceedings of the Sixth International Joint Conference on Natural Language Processing, 2013, pp. 534–542.

Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale
G. Skitalinskaya, J. Klaff, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–1729.

Learning to Flip the Bias of News Headlines
W.-F. Chen, H. Wachsmuth, K. Al Khatib, B. Stein, in: Proceedings of the 11th International Conference on Natural Language Generation, Association for Computational Linguistics, 2018, pp. 79–88.

Mining Crowdsourcing Problems from Discussion Forums of Workers
Z. Nouri, H. Wachsmuth, G. Engels, in: Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics, n.d., pp. 6264–6276.

Mining Supportive and Unsupportive Evidence from Facebook Using Anti-reconstruction of the Nuclear Power Plant as an Example
W.-F. Chen, L.-W. Ku, Y.-H. Lee, in: 2015 AAAI Spring Symposium Series, 2015.

Modeling Deliberative Argumentation Strategies on Wikipedia
K. Al Khatib, H. Wachsmuth, K. Lang, J. Herpel, M. Hagen, B. Stein, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, pp. 2545–2555.

Modeling Frames in Argumentation
Y. Ajjour, M. Alshomary, H. Wachsmuth, B. Stein, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, 2019, pp. 2915–2925.

Modeling Review Argumentation for Robust Sentiment Analysis
H. Wachsmuth, M. Trenkmann, B. Stein, G. Engels, in: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2014, pp. 553–564.

No Word Embedding Model Is Perfect: Evaluating the Representation  Accuracy for Social Bias in the Media
M. Spliethöver, M. Keiff, H. Wachsmuth, in: Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Association for Computational Linguistics, 2022.

On the Role of Knowledge in  Computational Argumentation
A. Lauscher, H. Wachsmuth, I. Gurevych, G. Glavas, Transactions of the Association for Computational Linguistics (2022).

On-The-Fly Computing -- Individualized IT-services in dynamic markets
C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim, On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023.

Optimal Scheduling of Information Extraction Algorithms
H. Wachsmuth, B. Stein, in: Proceedings of COLING 2012: Posters, 2012, pp. 1281–1290.

Overview of Touché 2020: Argument Retrieval
A. Bondarenko, M. Fröbe, M. Beloucif, L. Gienapp, Y. Ajjour, A. Panchenko, C. Biemann, B. Stein, H. Wachsmuth, M. Potthast, M. Hagen, in: CEUR Workshop Proceedings, 2020, pp. 384–395.

Overview of Touché 2021: Argument Retrieval
A. Bondarenko, L. Gienapp, M. Fröbe, M. Beloucif, Y. Ajjour, A. Panchenko, C. Biemann, B. Stein, H. Wachsmuth, M. Potthast, M. Hagen, in: Proceedings of the 43rd Annual European Conference on Information Retrieval Research, 2021, pp. 384–395.

Overview of Touché 2022: Argument Retrieval
A. Bondarenko, M. Fröbe, J. Kiesel, S. Syed, T. Gurcke, M. Beloucif, A. Panchenko, C. Biemann, B. Stein, H. Wachsmuth, M. Potthast, M. Hagen, in: Lecture Notes in Computer Science, Springer International Publishing, Cham, 2022.

Patterns of Argumentation Strategies across Topics
K. Al Khatib, H. Wachsmuth, M. Hagen, B. Stein, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 1362–1368.

PBlaman: performance blame analysis based on Palladio contracts
F. Brüseke, H. Wachsmuth, G. Engels, S. Becker, in: Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment, 2014, pp. 1975–2004.

Persuasiveness of News Editorials depending on Ideology and Personality
R. El Baff, K. Al-Khatib, B. Stein, H. Wachsmuth, in: Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES 2020), 2020, pp. 29–40.

Pipelines for Ad-hoc Large-scale Text Mining
H. Wachsmuth, Pipelines for Ad-Hoc Large-Scale Text Mining, 2015.

Pipelines Für Effiziente und Robuste Ad-hoc Textanalyse
H. Wachsmuth, in: Ausgezeichnete Informatikdissertationen 2015, 2016, pp. 329–338.

Playful User-Generated Treatment: A Novel Game Design Approach for VR Exposure Therapy
D. Alexandrovsky, G. Volkmar, M. Spliethöver, S. Finke, M. Herrlich, T. Döring, J.D. Smeddinck, R. Malaka, in: Proceedings of the Annual Symposium on Computer-Human Interaction in Play, Association for Computing Machinery, New York, NY, USA, 2020, pp. 32–45.

Political Speaker Transfer: Learning to Generate Text in the Styles of Barack Obama and Donald Trump
J. Bülling, Political Speaker Transfer: Learning to Generate Text in the Styles of Barack Obama and Donald Trump, 2021.

Proceedings of the 6th Workshop on Argument Mining
B. Stein, H. Wachsmuth, eds., Proceedings of the 6th Workshop on Argument Mining, Association for Computational Linguistics, Florence, Italy, 2019.

Propaganda Technique Detection Using Connotation Frames
V. Budanurmath, Propaganda Technique Detection Using Connotation Frames, 2022.

Reproducible Web Corpora
J. Kiesel, F. Kneist, M. Alshomary, B. Stein, M. Hagen, M. Potthast, Journal of Data and Information Quality (2018) 1–25.

RESOLVE: An Emotion Word Suggestion System Facilitates Language Learners’ Emotional Expressions
M.-H. Chen, W.-F. Chen, L.-W.  Ku, in: Proceedings of the AsiaCALL 2014, 2014.

Retrieval of the Best Counterargument without Prior Topic Knowledge
H. Wachsmuth, S. Syed, B. Stein, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, pp. 241–251.

SemEval-2018 Task 12: The Argument Reasoning Comprehension Task
I. Habernal, H. Wachsmuth, I. Gurevych, B. Stein, in: Proceedings of The 12th International Workshop on Semantic Evaluation, 2018, pp. 763–772.

Semi-Supervised Cleansing of Web Argument Corpora
J. Dorsch, H. Wachsmuth, in: Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 19–29.

Sentiment Flow - A General Model of Web Review Argumentation
H. Wachsmuth, J. Kiesel, B. Stein, in: J. Tsujii, J. Hajic (Eds.), Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 601–611.

Smart Teams: Simulating Large Robotic Swarms in Vast Environments
S. Arens, A. Buss, H. Deck, M. Dynia, M. Fischer, H. Hagedorn, P. Isaak, J. Kutylowski, F. Meyer auf der Heide, V. Nesterow, A. Ogiermann, B. Stobbe, T. Storm, H. Wachsmuth, in: Proceedings of the 4th International Symposium on Autonomous Minirobots for Research and Edutainment, Heinz Nixdorf Institut, University of Paderborn, Buenos Aires, Argentina, 2007, pp. 215–222.

Syntopical Graphs for Computational Argumentation Tasks
J. Barrow, R. Jain, N. Lipka, F. Dernoncourt, V. Morariu, V. Manjunatha, D. Oard, P. Resnik, H. Wachsmuth, in: Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), 2021, pp. 1583–1595.

Target Inference in Argument Conclusion Generation
M. Alshomary, S. Syed, M. Potthast, H. Wachsmuth, in: Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Association for Computational Linguistics, 2020, pp. 4334–4345.

Task Proposal: Abstractive Snippet Generation for Web Pages
S. Syed, W.-F. Chen, M. Hagen, B. Stein, H. Wachsmuth, M. Potthast, in: Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020), 2020, pp. 237–241.

Technology Enhanced Emotion Expression Learning
M.-H. Chen, W.-F. Chen, L.-W. Ku, in: Proceedings of the Sixth Joint Foreign Language Education and Technology Conference (FLEAT VI), 2015.

Text Analysis Pipelines - Towards Ad-hoc Large-scale Text Mining
H. Wachsmuth, Text Analysis Pipelines - Towards Ad-Hoc Large-Scale Text Mining, 2015.