Papers by Henning Wachsmuth
Employing Argumentation Knowledge Graphs for Neural Argument Generation (2021.acl-long)
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| Challenge: | Existing methods for generating arguments use end-to-end knowledge graphs or are controlled with respect to the argument's topic, aspects, or stance. |
| Approach: | They construct and populate three knowledge graphs and encode them into debate portals and relevant paragraphs from Wikipedia. |
| Outcome: | The proposed model produces arguments with superior quality than those generated without knowledge. |
Modeling Deliberative Argumentation Strategies on Wikipedia (P18-1)
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| Challenge: | Existing models for deliberative discussions have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. |
| Approach: | They propose to model argumentation strategies of deliberative discussions by annotating ongoing discussions with a label that can be used for move description. |
| Outcome: | The proposed model can predict arguments of participants in deliberative discussions using metadata from Wikipedia talk pages. |
Identifying the Human Values behind Arguments (2022.acl-long)
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| Challenge: | et al., 2003) examines human values in natural language arguments . authors provide a dataset of 5270 arguments from four geographical cultures . |
| Approach: | They propose a multi-level taxonomy of human values with 54 values and a dataset of 5270 arguments from four geographical cultures, manually annotated for human values. |
| Outcome: | The proposed model shows that human values are more diverse than previously thought . it shows that people disagree on the best course forward on controversial issues . |
Target Inference in Argument Conclusion Generation (2020.acl-main)
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| Challenge: | Existing approaches focus on generating single claims, but there are limitations. |
| Approach: | They propose to use a triplet neural network to infer a conclusion's target from premises' targets and a neural network for a new target. |
| Outcome: | The proposed approach outperforms baselines on two domains. |
Improving Argument Effectiveness Across Ideologies using Instruction-tuned Large Language Models (2024.findings-emnlp)
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| Challenge: | a study finds that different political ideologies hold different worldviews, which leads to contentious debates . argument effectiveness is improved by using instruction-tuned large language models . |
| Approach: | They propose to use instruction-tuned large language models to turn ineffective arguments into effective arguments for people with certain ideologies. |
| Outcome: | The proposed methods improve argument effectiveness for liberals by rewriting arguments using three LLM methods. |
Intrinsic Quality Assessment of Arguments (2020.coling-main)
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| Challenge: | Several quality dimensions of natural language arguments have been investigated. |
| Approach: | They propose to use a computational method to assess 15 quality dimensions of arguments by learning only from an argument's text. |
| Outcome: | The proposed approach achieves moderate but significant learning success for most dimensions. |
A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality (2024.naacl-long)
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| Challenge: | Existing arguments mining corpus with ground-truth quality annotations is lacking . authors propose baseline approaches to argument mining and essay scoring . |
| Approach: | They propose to use argumentative structure to support argumentative writing . they use an annotated german corpus to analyze interactions between the two tasks . |
| Outcome: | The proposed methods can be used to support argumentative writing . they analyze interactions between argumentative structure and quality annotations . |
Counter-Argument Generation by Attacking Weak Premises (2021.findings-acl)
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| Challenge: | a recent work explores the generation of counter-arguments by undermining one of its premises . identifying the argument's weak premises is key to effective countering, we hypothesize . |
| Approach: | They propose a pipeline approach that first assesses the argument's weak premises and generates a counter-argument undermining the weakest among them. |
| Outcome: | The proposed approach undermins arguments by attacking weak premises . human annotators favor the proposed approach over state-of-the-art approaches . |
Argument Quality Assessment in the Age of Instruction-Following Large Language Models (2024.lrec-main)
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Henning Wachsmuth, Gabriella Lapesa, Elena Cabrio, Anne Lauscher, Joonsuk Park, Eva Maria Vecchi, Serena Villata, Timon Ziegenbein
| Challenge: | Argument quality assessment is critical for opinion formation, decision making, writing education, and the like. |
| Approach: | They propose to use large language models to leverage knowledge across contexts to enable a much more reliable assessment. |
| Outcome: | The proposed approach improves the quality of argumentation and the ability to leverage knowledge across contexts. |
Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation (2022.tacl-1)
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| Challenge: | Existing research on argumentation models does not provide a systematic overview of the types of knowledge required in CA tasks. |
| Approach: | They propose a taxonomy of the types of knowledge required in CA tasks . authors propose exploitation of these knowledge types for four main research areas . |
| Outcome: | The proposed taxonomy proposes a systematic overview of the types of knowledge required in CA tasks. |
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants (N18-1)
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| Challenge: | Existing methods for analyzing warrants in natural language arguments are insufficient. |
| Approach: | They propose a method for reconstructing warrants from news comments . they use a crowdsourcing process to obtain warrants for 2k authentic arguments . |
| Outcome: | The proposed method will define a substantial step towards automatic warrant reconstruction. |
Towards a Perspectivist Turn in Argument Quality Assessment (2025.naacl-long)
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| Challenge: | Argument quality is a key aspect of computational argumentation (CA), but it still exhibits a high degree of subjectivity in perception. |
| Approach: | They propose to use a multi-layered classification to target two aspects of argument quality in a systematic review of NLP datasets. |
| Outcome: | The proposed model improves the quality of annotators and their ability to be used in perspectivist research. |
Detecting Media Bias in News Articles using Gaussian Bias Distributions (2020.findings-emnlp)
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| Challenge: | a new study shows that media bias is not only about honesty or accuracy, but also about taste or preference. |
| Approach: | They propose to use second-order information to detect media bias in articles . they propose to analyze the frequency, positions, and sequential order of biased statements . |
| Outcome: | The proposed model outperforms other models that use second-order information on biased statements on an existing media bias dataset. |
Generating Informative Conclusions for Argumentative Texts (2021.findings-acl)
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| Challenge: | Argumentative texts often omit explicit conclusions, expecting readers to infer them rather . a corpus of 136,996 arguments is compiled and used to generate informative conclusions . |
| Approach: | They propose to generate informative conclusions from a large-scale corpus of argumentative texts . they propose to use argumentative knowledge to augment the corpus and refine the model . |
| Outcome: | The proposed corpus of argumentative texts and their conclusions is compiled and analyzed . the results show that the proposed model is informative and concise . |
Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions (2025.findings-emnlp)
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Meghdut Sengupta, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Debanjan Ghosh, Henning Wachsmuth
| Challenge: | Metaphorical language is prevalent in everyday communication, often used unconsciously, as in "rising crime" incorporating conceptual metaphors improves performance on downstream tasks that implicitly require correct metaphor interpretation, such as NLI. |
| Approach: | They extend metaphorical texts in an existing NLI dataset by source and target domains and conduct an ablation study using Shapley values and interactions to assess the extent to which LLMs interpret metaphorical language correctly in NLI. |
| Outcome: | The proposed method improves performance on natural language inference tasks with metaphoric input. |
No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media (2022.findings-emnlp)
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| Challenge: | Recent work has relied on word embedding bias measures, such as WEAT, but these methods can be inaccurate due to several representation issues, such low-resource settings and token frequency differences. |
| Approach: | They propose to use WEAT to quantify social bias in US online news articles and embed embedding algorithms to account for the aforementioned issues. |
| Outcome: | The proposed algorithms do not match the literature, but they reduce the gap. |
Controlled Neural Sentence-Level Reframing of News Articles (2021.findings-emnlp)
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| Challenge: | a news article is framed from a specific perspective, but reframing can be difficult . a framed article can be used to communicate with opposing camps of audiences . |
| Approach: | They propose to reframe news articles using a media frame corpus to achieve this . they propose three strategies to train neural models for reframing . |
| Outcome: | The proposed techniques maintain coherence of sentences and reframe them correctly . the proposed techniques are effective but have tradeoffs . |
Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms (2023.findings-emnlp)
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| Challenge: | Existing methods to identify highlighted aspects of a metaphorical sentence are not yet available. |
| Approach: | They propose to identify the main aspect highlighted in a metaphorical sentence by using a joint learning approach and a continual learning approach. |
| Outcome: | The proposed approach improves predicting highlighted aspects and source domains compared to baselines on an existing corpus. |
Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation (N18-1)
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| Challenge: | Existing research lacks solid empirical investigation of typology of ad hominem arguments and their potential causes. |
| Approach: | They propose to perform several large-scale annotation studies and experiment with various neural architectures to validate hypotheses such as controversy or reasonableness. |
| Outcome: | The proposed model identifies the ad hominem fallacy and its possible causes using explainable neural network architectures. |
Analyzing the Persuasive Effect of Style in News Editorial Argumentation (2020.acl-main)
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| Challenge: | Existing research has investigated the persuasive effect of content and style on argumentative content. |
| Approach: | They compare the style of news editorials with ideology-specific effect annotations to find out how important it is to achieve persuasiveness. |
| Outcome: | The proposed method shows that conservative readers are resistant to style on liberal editorials, whereas conservative readers resist style on conservatives. |
“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations (2022.coling-1)
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| Challenge: | a new corpus of dialogical explanations is created to help explainable AI . a linguistic analysis of 65 transcribed English dialogues shows that explanations are co-constructed in a dialogue between the explainer and the explainee . |
| Approach: | They propose a corpus of dialogical explanations that are co-constructed in a dialogue . they analyze linguistic patterns of explainers and explainees and explore differences . |
| Outcome: | The proposed corpus of dialogical explanations enables NLP research on how humans explain . the analysis shows that sequence information helps predicting topics, acts, and moves effectively . |
Syntopical Graphs for Computational Argumentation Tasks (2021.acl-long)
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Joe Barrow, Rajiv Jain, Nedim Lipka, Franck Dernoncourt, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik, Henning Wachsmuth
| Challenge: | adler and van Doren (1940) proposed a formalized manual process for understanding a topic based on multiple viewpoints. |
| Approach: | They propose a syntopical reading process that emphasizes comparing and contrasting viewpoints to improve topic understanding. |
| Outcome: | The proposed method outperforms approaches that do not use collection-level information. |
Modeling the Quality of Dialogical Explanations (2024.lrec-main)
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Milad Alshomary, Felix Lange, Meisam Booshehri, Meghdut Sengupta, Philipp Cimiano, Henning Wachsmuth
| Challenge: | Existing studies have focused on the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. |
| Approach: | They construct a corpus of 399 reddit dialogues and analyze interaction flows and explainee quality using two language models that can handle long inputs. |
| Outcome: | The proposed model predicts that the interaction flows between the explainer and the explainee correlate with the quality of the explanations in terms of a successful understanding on the explain's side. |
Visualization of the Topic Space of Argument Search Results in args.me (D18-2)
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Yamen Ajjour, Henning Wachsmuth, Dora Kiesel, Patrick Riehmann, Fan Fan, Giuliano Castiglia, Rosemary Adejoh, Bernd Fröhlich, Benno Stein
| Challenge: | args.me is the first search engine for controversial topics . it ranks pro and con arguments by their relevance to a topic . |
| Approach: | They propose a visualization interface for result exploration that provides an overview of main aspects in a barycentric coordinate system. |
| Outcome: | The proposed search engine is the first dedicated argument search engine on the web. |
Conclusion-based Counter-Argument Generation (2023.eacl-main)
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| Challenge: | Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. |
| Approach: | They propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. |
| Outcome: | The proposed approach generates more relevant and stance-adhering counters than strong baselines. |
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition (D19-50)
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| Challenge: | Existing methods to detect abusive language only train one classifier for the whole variety of offending . a new method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |
| Approach: | a new method is proposed to distinguish explicitly abusive cases from the more "shadowed" ones . the researchers extend a lexicon of abusive terms to include new obfuscations of abusive words . |
| Outcome: | a new method can distinguish explicitly abusive cases from the more "shadowed" ones . the method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |
Reference-guided Style-Consistent Content Transfer (2024.lrec-main)
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| Challenge: | Text style transfer involves changing the style of a text while preserving its original style. |
| Approach: | They propose a task of style-consistent content transfer which involves modifying a text’s content based on a provided reference statement while preserving its original style. |
| Outcome: | The proposed approach meets three important conditions: reference faithfulness, style adherence, and coherence. |
ArgBench: Benchmarking LLMs on Computational Argumentation Tasks (2026.findings-acl)
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| Challenge: | Argumentation skills are an essential toolkit for large language models (LLMs). |
| Approach: | They propose a benchmark to evaluate the generalizability of five LLM families across 46 computational argumentation tasks. |
| Outcome: | The proposed benchmark evaluates the generalizability of five LLM families across 46 computational argumentation tasks covering mining arguments, assessing perspectives, evaluating argument quality, reasoning about arguments, and generating arguments. |
Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness (2024.findings-acl)
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| Challenge: | Existing studies have studied dialect-related fairness for aspects like hate speech, but other aspects of biased language remain unexplored. |
| Approach: | They propose a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. |
| Outcome: | The proposed approach improves dialect learning and detects biases more reliably. |
Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2024.lrec-tutorials)
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| Challenge: | a tutorial on computational argumentation is updated to address the problem of argument quality . argument quality is a field of interdisciplinary research that connects natural language processing to social sciences . |
| Approach: | They present an updated version of the EACL 2023 tutorial on argument quality . they will focus on the notions of argument quality across disciplines . |
| Outcome: | The updated version of the EACL 2023 tutorial focuses on argument quality assessment . the authors will focus on the interface between Argument Mining and Deliberation Theory . |
Mining, Assessing, and Improving Arguments in NLP and the Social Sciences (2023.eacl-tutorials)
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| Challenge: | a tutorial on argument quality assessment will focus on what makes an argument good or bad . argument quality is a field encompassing varying tasks on the automated analysis and synthesis of natural language arguments. |
| Approach: | This tutorial will focus on the assessment of argument quality across disciplines . authors will involve participants in annotation studies on the quality assessment . |
| Outcome: | The tutorial will focus on the assessment of argument quality across disciplines . it will involve participants in two annotation studies on the quality assessment and the improvement of quality . |
Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning (2026.acl-long)
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| Challenge: | Comparing human-like edits to LLMs, we observe a mismatch in editing strategies. |
| Approach: | They propose a reinforcement learning approach that teaches LLMs human-like editing to improve the appropriateness of arguments. |
| Outcome: | The proposed approach outperforms baselines and the state of the art in human-like editing, with multi-round editing achieving appropriateness close to full rewriting. |
Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection (2025.naacl-long)
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Maximilian Spliethöver, Tim Knebler, Fabian Fumagalli, Maximilian Muschalik, Barbara Hammer, Eyke Hüllermeier, Henning Wachsmuth
| Challenge: | Existing prompting techniques for large language models depend on several parameters, such as the task, language model, and context provided. |
| Approach: | They propose an adaptive prompting approach that predicts the optimal prompt composition ad-hoc for a given input. |
| Outcome: | The proposed approach ensures high detection performance and is best in several settings. |
ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have been trained to follow instructions for many NLP tasks, including several tasks from computational argumentation (CA), the computational analysis and synthesis of natural language arguments. |
| Approach: | They propose a specialized instruction fine-tuning for the domain of computational argumentation (CA) they synthesized 52k CA-related instructions and used them to train a CA-specialized instruction-following LLM. |
| Outcome: | The proposed benchmarks show that the LLMs can tackle unseen and seen tasks while maintaining generalization capabilities. |
LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback (2024.acl-long)
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| Challenge: | Creating trusted and safe online spaces for people with different backgrounds and opinions is a challenge for social media platforms. |
| Approach: | They propose a reinforcement learning-based rewriting approach that balances content preservation and appropriateness based on existing classifiers. |
| Outcome: | The proposed approach significantly outperforms baselines including few-shot learning, prompting, and humans. |
To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support (2023.acl-long)
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| Challenge: | assessing whether and how different claims in a text need to be revised is a hard task, especially for novice writers. |
| Approach: | They propose a sampling strategy based on revision distance to capture differences between versions of the same text. |
| Outcome: | The proposed sampling strategy can be done without additional annotations and judgments. |
Modeling Appropriate Language in Argumentation (2023.acl-long)
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| Challenge: | Existing research on offensive language has not been systematically addressed in debates . a new taxonomy of 14 dimensions determines inappropriate language in online discussions . |
| Approach: | They propose a taxonomy of 14 dimensions that determine inappropriate language in online discussions . they build on arguments quality corpora and annotate them on a corpus of 2191 arguments . |
| Outcome: | The proposed taxonomy covers the concept of appropriateness comprehensively, showing plausible correlations with argument quality dimensions. |
Mind the Gap: Automated Corpus Creation for Enthymeme Detection and Reconstruction in Learner Arguments (2023.findings-emnlp)
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| Challenge: | Argumentative writing is an essential skill that can be challenging to acquire. |
| Approach: | They propose two new tasks to identify gaps in arguments and fill such gaps by deleting ADUs from argumentative text. |
| Outcome: | The proposed methods reduce argument quality and produce arguments that are natural to those written by learners. |
Analyzing the Use of Metaphors in News Editorials for Political Framing (2024.naacl-long)
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| Challenge: | Existing studies on the use of metaphors in political discourses are largely unexplored. |
| Approach: | They propose to use a dataset to study the use of metaphors in political discourses . they identify single and composite metaphors and provide annotations of their source and target domains based on the corpus . |
| Outcome: | The proposed dataset consists of 300 news editorials with single and composite metaphors and annotations of the source and target domains for each metaphor. |
Belief-based Generation of Argumentative Claims (2021.eacl-main)
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| Challenge: | Existing methods to generate argument with the ability to encode beliefs are limited by the noise generated by the automatic collection of bag-of-words. |
| Approach: | They propose to augment argument generation technology with ability to encode beliefs . they model users' beliefs via their stances on big issues and extend text generation models with extra input reflecting user's beliefs. |
| Outcome: | The proposed approach is low in effectiveness because of the noise produced by the automatic collection of bag-of-words. |
Mining Crowdsourcing Problems from Discussion Forums of Workers (2020.coling-main)
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| Challenge: | Among the most widely used platforms are Upwork, Appen, and above all Amazon Mechanical Turk (MTurk) which host annotation tasks and collect huge sets of annotated data from workers. |
| Approach: | They propose to use topic modeling to analyze workers' complaints from a new English corpus of workers’ forum discussions to identify problems in task design, task operation, and task evaluation that workers face with requesters in crowdsourcing processes. |
| Outcome: | The findings form the basis for future research on how to improve crowdsourcing processes. |
The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments (2022.acl-long)
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| Challenge: | Existing arguments that focus on shared values are based on prior beliefs and morals, but little research has been done on the effectiveness of these proxies. |
| Approach: | They propose a system that automatically generates arguments focusing on different morals and ask liberals and conservatives to evaluate the impact of these arguments. |
| Outcome: | The proposed system generates arguments focusing on different morals, and the results are compared with existing arguments. |
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments (2024.lrec-main)
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Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary, Maximilian Heinrich, Nicolas Handke, Xiaoni Cai, Valentin Barriere, Doratossadat Dastgheib, Omid Ghahroodi, MohammadAli SadraeiJavaheri, Ehsaneddin Asgari, Lea Kawaletz, Henning Wachsmuth, Benno Stein
| Challenge: | Cultural norms can influence the prioritization of values, leading to distinct perspectives on debatable topics. |
| Approach: | They present a Touché23-ValueEval dataset that annotates 4780 new arguments and annotated 54 human values. |
| Outcome: | The Touché23-ValueEval dataset doubles the original Webis-ArgValués-22 dataset to 9324 arguments. |
Argumentation Synthesis following Rhetorical Strategies (C18-1)
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| Challenge: | Existing argument mining studies focus on logical structure of arguments, identifying their units and relations, and the effects of logical and emotional arguments across audiences. |
| Approach: | They propose to use rhetorical strategies to synthesize argumentative texts with different strategies. |
| Outcome: | The proposed model shows that the experts agree significantly more on selection when following the same strategy. |
Retrieval of the Best Counterargument without Prior Topic Knowledge (P18-1)
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| Challenge: | ad-hominem attacks are the most common form of argumentation in real life . |
| Approach: | They hypothesize that the best counterargument invokes the same aspects as the argument while having the opposite stance. |
| Outcome: | The proposed model is independent from the topic at hand, i.e., it applies to arbitrary arguments. |
Modeling Frames in Argumentation (D19-1)
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| Challenge: | In argumentation, framing is used to emphasize a specific aspect of a topic while concealing others. |
| Approach: | They propose an unsupervised method for framing arguments into non-overlapping frames . authors propose a corpus of 12, 326 debate-portal arguments organized along the frames of debates' topics . |
| Outcome: | The proposed method outperforms baselines on the argumentation task by 0.28 points. |
Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale (2021.eacl-main)
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| Challenge: | Existing research on predicting argument quality based on subjective assessments of human annotators ignores this limitation. |
| Approach: | They propose to compare different revisions of the same claim to assess their quality . they use logistic regression and transformer-based neural networks to learn quality indicators . |
| Outcome: | The proposed tasks show that the learned indicators generalize well across topics. |