@inproceedings{61190, author = {{Sengupta, Meghdut and Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Hüllermeier, Eyke and Ghosh, Debanjan and Wachsmuth, Henning}}, booktitle = {{Accepted in Findings }}, publisher = {{EMNLP }}, title = {{{Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions}}}, year = {{2025}}, } @inproceedings{61234, abstract = {{The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee's background and needs, recent research focused on co-constructive explanation dialogues, where an explainer continuously monitors the explainee's understanding and adapts their explanations dynamically. We investigate the ability of large language models (LLMs) to engage as explainers in co-constructive explanation dialogues. In particular, we present a user study in which explainees interact with an LLM in two settings, one of which involves the LLM being instructed to explain a topic co-constructively. We evaluate the explainees' understanding before and after the dialogue, as well as their perception of the LLMs' co-constructive behavior. Our results suggest that LLMs show some co-constructive behaviors, such as asking verification questions, that foster the explainees' engagement and can improve understanding of a topic. However, their ability to effectively monitor the current understanding and scaffold the explanations accordingly remains limited.}}, author = {{Fichtel, Leandra and Spliethöver, Maximilian and Hüllermeier, Eyke and Jimenez, Patricia and Klowait, Nils and Kopp, Stefan and Ngonga Ngomo, Axel-Cyrille and Robrecht, Amelie and Scharlau, Ingrid and Terfloth, Lutz and Vollmer, Anna-Lisa and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue}}, publisher = {{Association for Computational Linguistics}}, title = {{{Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues}}}, year = {{2025}}, } @inproceedings{59856, abstract = {{Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as explicit reasoning steps. However, the success of techniques depends on various parameters, such as the task, language model, and context provided. Finding an effective prompt is, therefore, often a trial-and-error process. Most existing approaches to automatic prompting aim to optimize individual techniques instead of compositions of techniques and their dependence on the input. To fill this gap, we propose an adaptive prompting approach that predicts the optimal prompt composition ad-hoc for a given input. We apply our approach to social bias detection, a highly context-dependent task that requires semantic understanding. We evaluate it with three large language models on three datasets, comparing compositions to individual techniques and other baselines. The results underline the importance of finding an effective prompt composition. Our approach robustly ensures high detection performance, and is best in several settings. Moreover, first experiments on other tasks support its generalizability.}}, author = {{Spliethöver, Maximilian and Knebler, Tim and Fumagalli, Fabian and Muschalik, Maximilian and Hammer, Barbara and Hüllermeier, Eyke and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)}}, editor = {{Chiruzzo, Luis and Ritter, Alan and Wang, Lu}}, isbn = {{979-8-89176-189-6}}, pages = {{2421–2449}}, publisher = {{Association for Computational Linguistics}}, title = {{{Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection}}}, year = {{2025}}, } @inproceedings{55338, abstract = {{Metaphorical language is a pivotal element inthe realm of political framing. Existing workfrom linguistics and the social sciences providescompelling evidence regarding the distinctivenessof conceptual framing for politicalideology perspectives. However, the nature andutilization of metaphors and the effect on audiencesof different political ideologies withinpolitical discourses are hardly explored. Toenable research in this direction, in this workwe create a dataset, originally based on newseditorials and labeled with their persuasive effectson liberals and conservatives and extend itwith annotations pertaining to metaphorical usageof language. To that end, first, we identifyall single metaphors and composite metaphors.Secondly, we provide annotations of the sourceand target domains for each metaphor. As aresult, our corpus consists of 300 news editorialsannotated with spans of texts containingmetaphors and the corresponding domains ofwhich these metaphors draw from. Our analysisshows that liberal readers are affected bymetaphors, whereas conservatives are resistantto them. Both ideologies are affected differentlybased on the metaphor source and targetcategory. For example, liberals are affected bymetaphors in the Darkness {&} Light (e.g., death)source domains, where as the source domain ofNature affects conservatives more significantly.}}, author = {{Sengupta, Meghdut and El Baff, Roxanne and Alshomary, Milad and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)}}, editor = {{Duh, Kevin and Gomez, Helena and Bethard, Steven}}, pages = {{3621–3631}}, publisher = {{Association for Computational Linguistics}}, title = {{{Analyzing the Use of Metaphors in News Editorials for Political Framing}}}, year = {{2024}}, } @inproceedings{55404, abstract = {{Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an explainer discusses a concept or phenomenon of interest with an explainee. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee{’}s side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum {Explain Like I am Five} and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.}}, author = {{Alshomary, Milad and Lange, Felix and Booshehri, Meisam and Sengupta, Meghdut and Cimiano, Philipp and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}}, editor = {{Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen}}, pages = {{11523–11536}}, publisher = {{ELRA and ICCL}}, title = {{{Modeling the Quality of Dialogical Explanations}}}, year = {{2024}}, } @inproceedings{58722, abstract = {{Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.}}, author = {{Spliethöver, Maximilian and Menon, Sai Nikhil and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: ACL 2024}}, editor = {{Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek}}, pages = {{9294–9313}}, publisher = {{Association for Computational Linguistics}}, title = {{{Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness}}}, doi = {{10.18653/v1/2024.findings-acl.553}}, year = {{2024}}, } @inproceedings{34083, abstract = {{In the context of language learning, feedback comment generation is the task of generating hints or explanatory notes for learner texts that help understand why a part of text is erroneous. This paper presents our approach to the Feedback Comment Generation Shared Task, collocated with the 16th International Natural Language Generation Conference (INLG 2023). The approach augments the generation of feedback comments by a self-supervised identification of feedback types in a multitask-learning setting. Within the shared task, other approaches performed more effective, yet the combined modeling of feedback type classification and feedback comment generation is superior to performing feedback generation only.}}, author = {{Stahl, Maja and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th International Natural Language Generation Conference}}, title = {{{Identifying Feedback Types to Augment Feedback Comment Generation}}}, year = {{2023}}, } @book{45863, abstract = {{In the proposal for our CRC in 2011, we formulated a vision of markets for IT services that describes an approach to the provision of such services that was novel at that time and, to a large extent, remains so today: „Our vision of on-the-fly computing is that of IT services individually and automatically configured and brought to execution from flexibly combinable services traded on markets. At the same time, we aim at organizing markets whose participants maintain a lively market of services through appropriate entrepreneurial actions.“ Over the last 12 years, we have developed methods and techniques to address problems critical to the convenient, efficient, and secure use of on-the-fly computing. Among other things, we have made the description of services more convenient by allowing natural language input, increased the quality of configured services through (natural language) interaction and more efficient configuration processes and analysis procedures, made the quality of (the products of) providers in the marketplace transparent through reputation systems, and increased the resource efficiency of execution through reconfigurable heterogeneous computing nodes and an integrated treatment of service description and configuration. We have also developed network infrastructures that have a high degree of adaptivity, scalability, efficiency, and reliability, and provide cryptographic guarantees of anonymity and security for market participants and their products and services. To demonstrate the pervasiveness of the OTF computing approach, we have implemented a proof-of-concept for OTF computing that can run typical scenarios of an OTF market. We illustrated the approach using a cutting-edge application scenario – automated machine learning (AutoML). Finally, we have been pushing our work for the perpetuation of On-The-Fly Computing beyond the SFB and sharing the expertise gained in the SFB in events with industry partners as well as transfer projects. This work required a broad spectrum of expertise. Computer scientists and economists with research interests such as computer networks and distributed algorithms, security and cryptography, software engineering and verification, configuration and machine learning, computer engineering and HPC, microeconomics and game theory, business informatics and management have successfully collaborated here.}}, author = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{247}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}}, doi = {{10.17619/UNIPB/1-1797}}, volume = {{412}}, year = {{2023}}, } @inproceedings{55406, abstract = {{Metaphorical language, such as {“}spending time together{”}, projects meaning from a source domain (here, $money$) to a target domain ($time$). Thereby, it highlights certain aspects of the target domain, such as the $effort$ behind the time investment. Highlighting aspects with metaphors (while hiding others) bridges the two domains and is the core of metaphorical meaning construction. For metaphor interpretation, linguistic theories stress that identifying the highlighted aspects is important for a better understanding of metaphors. However, metaphor research in NLP has not yet dealt with the phenomenon of highlighting. In this paper, we introduce the task of identifying the main aspect highlighted in a metaphorical sentence. Given the inherent interaction of source domains and highlighted aspects, we propose two multitask approaches - a joint learning approach and a continual learning approach - based on a finetuned contrastive learning model to jointly predict highlighted aspects and source domains. We further investigate whether (predicted) information about a source domain leads to better performance in predicting the highlighted aspects, and vice versa. Our experiments on an existing corpus suggest that, with the corresponding information, the performance to predict the other improves in terms of model accuracy in predicting highlighted aspects and source domains notably compared to the single-task baselines.}}, author = {{Sengupta, Meghdut and Alshomary, Milad and Scharlau, Ingrid and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2023}}, editor = {{Bouamor, Houda and Pino, Juan and Bali, Kalika}}, pages = {{4636–4659}}, publisher = {{Association for Computational Linguistics}}, title = {{{Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms}}}, doi = {{10.18653/v1/2023.findings-emnlp.308}}, year = {{2023}}, } @inproceedings{58723, abstract = {{In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. 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. In this paper, we hypothesize that the key to effective counter-argument generation is to explicitly model the argument‘s conclusion and to ensure that the stance of the generated counter is opposite to that conclusion. In particular, we propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. The approach employs a stance-based ranking component that selects the counter from a diverse set of generated candidates whose stance best opposes the generated conclusion. In both automatic and manual evaluation, we provide evidence that our approach generates more relevant and stance-adhering counters than strong baselines.}}, author = {{Alshomary, Milad and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics}}, editor = {{Vlachos, Andreas and Augenstein, Isabelle}}, pages = {{957–967}}, publisher = {{Association for Computational Linguistics}}, title = {{{Conclusion-based Counter-Argument Generation}}}, doi = {{10.18653/v1/2023.eacl-main.67}}, year = {{2023}}, } @inproceedings{33004, author = {{Wachsmuth, Henning and Alshomary, Milad}}, booktitle = {{Proceedings of the 29th International Conference on Computational Linguistics}}, pages = {{344 -- 354}}, title = {{{"Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain}}}, year = {{2022}}, } @article{34049, author = {{Lauscher, Anne and Wachsmuth, Henning and Gurevych, Iryna and Glavaš, Goran}}, journal = {{Transactions of the Association for Computational Linguistics}}, title = {{{On the Role of Knowledge in Computational Argumentation}}}, year = {{2022}}, } @inproceedings{22157, author = {{Kiesel, Johannes and Alshomary, Milad and Handke, Nicolas and Cai, Xiaoni and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}}, pages = {{4459 -- 4471}}, title = {{{Identifying the Human Values behind Arguments}}}, year = {{2022}}, } @inproceedings{34082, abstract = {{Gender bias may emerge from an unequal representation of agency and power, for example, by portraying women frequently as passive and powerless ("She accepted her future'') and men as proactive and powerful ("He chose his future''). When language models learn from respective texts, they may reproduce or even amplify the bias. An effective way to mitigate bias is to generate counterfactual sentences with opposite agency and power to the training. Recent work targeted agency-specific verbs from a lexicon to this end. We argue that this is insufficient, due to the interaction of agency and power and their dependence on context. In this paper, we thus develop a new rewriting model that identifies verbs with the desired agency and power in the context of the given sentence. The verbs' probability is then boosted to encourage the model to rewrite both connotations jointly. According to automatic metrics, our model effectively controls for power while being competitive in agency to the state of the art. In our evaluation, human annotators favored its counterfactuals in terms of both connotations, also deeming its meaning preservation better.}}, author = {{Stahl, Maja and Spliethöver, Maximilian and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science}}, location = {{Abu Dhabi, United Arab Emirates}}, title = {{{To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation}}}, year = {{2022}}, } @inproceedings{33274, author = {{Chen, Wei-Fan and Chen, Mei-Hua and Mudgal, Garima and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022)}}, pages = {{51 -- 61}}, title = {{{Analyzing Culture-Specific Argument Structures in Learner Essays}}}, year = {{2022}}, } @inproceedings{31068, author = {{Chen, Mei-Hua and Mudgal, Garima and Chen, Wei-Fan and Wachsmuth, Henning}}, booktitle = {{EUROCALL}}, title = {{{Investigating the argumentation structures of EFL learners from diverse language backgrounds}}}, year = {{2022}}, } @inproceedings{55337, abstract = {{As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series 5 Levels, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively.}}, author = {{Wachsmuth, Henning and Alshomary, Milad}}, booktitle = {{Proceedings of the 29th International Conference on Computational Linguistics}}, editor = {{Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon}}, pages = {{344–354}}, publisher = {{International Committee on Computational Linguistics}}, title = {{{“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations}}}, year = {{2022}}, } @inproceedings{34067, author = {{Sengupta, Meghdut and Alshomary, Milad and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 2022 Workshop on Figurative Language Processing}}, title = {{{Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning}}}, year = {{2022}}, } @inproceedings{32247, author = {{Alshomary, Milad and Rieskamp, Jonas and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 9th International Conference on Computational Models of Argument}}, pages = {{21 -- 31}}, title = {{{Generating Contrastive Snippets for Argument Search}}}, doi = {{http://dx.doi.org/10.3233/FAIA220138}}, year = {{2022}}, } @inproceedings{30840, author = {{Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}}, pages = {{8782 -- 8797}}, title = {{{The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments}}}, year = {{2022}}, }