@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}}, } @misc{29000, abstract = {{This thesis aims to provide a bidirectional chatbot solution for the requirement engineering process. The Sonderforschungsbereich (SFB) 901 intends to provide the composition of software service On-the-Fly (OTF). The sub-project (B1) of the SFB 901 project deals with the parameters of service configuration. OTF Computing aims to eradicate the dependency on the requirement engineers for the software development process. However, there is no existing bidirectional chatbot solution that analyses user software requirements and provides viable suggestions to the user regarding their service. Previously, CORDULA chatbot was developed to analyze the software requirements but cannot keep the conversation’s context. The Rasa framework is integrated with the knowledge base to solve the issue, the knowledge base provides domain-specific knowledge to the chatbot. The software description is passed through the natural language understanding process to give consciousness to the chatbot. This process involves various machine learning models, including app family classification, to correctly identify the domain for user OTF service. The statistical models like naïve Bayes, kNN and SVM are compared with transformer models for this classification task. Furthermore, the entities (functional requirements) are also separated from the user description. The chatbot provides the suggestion of requirements from the preliminary service template with the support of the knowledge base. Furthermore, the generated response is compared with the state-of-the-art DialoGPT transformer model and ChatterBot conversational library. These models are trained over the software development related conversational dataset. All the responses are ranked using the DialoRPT model, and the BLEU score to evaluates the models’ responses. Moreover, the chatbot mod- els are tested with human participants, they used and scored the chatbot responses based on effectiveness, efficiency and satisfaction. The overall response accuracy is also measured by averaging the user approval over the generated responses.}}, author = {{Ahmed, Mobeen}}, title = {{{Knowledge Base Enhanced & User-centric Dialogue Design for OTF Computing}}}, year = {{2022}}, } @misc{45790, author = {{Palushi, Juela}}, title = {{{Domain-aware Text Professionalization using Sequence-to-Sequence Neural Networks}}}, year = {{2022}}, } @misc{45789, author = {{Budanurmath, Vinaykumar}}, title = {{{Propaganda Technique Detection Using Connotation Frames}}}, 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}}, } @inproceedings{20115, author = {{Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics}}, pages = {{1718--1729}}, title = {{{Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale}}}, year = {{2021}}, } @inproceedings{3774, author = {{Bondarenko, Alexander and Gienapp, Lukas and Fröbe, Maik and Beloucif, Meriem and Ajjour, Yamen and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and Hagen, Matthias}}, booktitle = {{Proceedings of the 43rd annual European Conference on Information Retrieval Research}}, pages = {{384--395}}, title = {{{Overview of Touché 2021: Argument Retrieval}}}, year = {{2021}}, } @inproceedings{23708, author = {{Nouri, Zahra and Gadiraju, Ujwal and Engels, Gregor and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 32nd ACM Conference on Hypertext and Social Media}}, pages = {{165--175}}, title = {{{What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing}}}, year = {{2021}}, } @inproceedings{22156, abstract = {{Word embedding models reflect bias towards genders, ethnicities, and other social groups present in the underlying training data. Metrics such as ECT, RNSB, and WEAT quantify bias in these models based on predefined word lists representing social groups and bias-conveying concepts. How suitable these lists actually are to reveal bias - let alone the bias metrics in general - remains unclear, though. In this paper, we study how to assess the quality of bias metrics for word embedding models. In particular, we present a generic method, Bias Silhouette Analysis (BSA), that quantifies the accuracy and robustness of such a metric and of the word lists used. Given a biased and an unbiased reference embedding model, BSA applies the metric systematically for several subsets of the lists to the models. The variance and rate of convergence of the bias values of each model then entail the robustness of the word lists, whereas the distance between the models' values gives indications of the general accuracy of the metric with the word lists. We demonstrate the behavior of BSA on two standard embedding models for the three mentioned metrics with several word lists from existing research.}}, author = {{Spliethöver, Maximilian and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21}}, location = {{Online}}, pages = {{552--559}}, title = {{{Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models}}}, doi = {{10.24963/ijcai.2021/77}}, year = {{2021}}, } @inproceedings{22158, author = {{Syed, Shahbaz and Al-Khatib, Khalid and Alshomary, Milad and Wachsmuth, Henning and Potthast, Martin}}, booktitle = {{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}}, pages = {{3482--3493}}, title = {{{Generating Informative Conclusions for Argumentative Texts}}}, year = {{2021}}, } @inproceedings{22159, author = {{Barrow, Joe and Jain, Rajiv and Lipka, Nedim and Dernoncourt, Franck and Morariu, Vlad and Manjunatha, Varun and Oard, Douglas and Resnik, Philip and Wachsmuth, Henning}}, booktitle = {{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)}}, pages = {{1583--1595}}, title = {{{Syntopical Graphs for Computational Argumentation Tasks}}}, year = {{2021}}, } @inproceedings{22160, author = {{Al-Khatib, Khalid and Trautner, Lukas and Wachsmuth, Henning and Hou, Yufang and Stein, Benno}}, booktitle = {{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)}}, pages = {{4744--4754}}, title = {{{Employing Argumentation Knowledge Graphs for Neural Argument Generation}}}, year = {{2021}}, } @inproceedings{22448, author = {{Kiesel, Johannes and Spina, Damiano and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 2021 Conversational User Interfaces Conference}}, pages = {{1--5}}, title = {{{The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases}}}, year = {{2021}}, } @article{22449, author = {{Alshomary, Milad and Wachsmuth, Henning}}, journal = {{Patterns}}, number = {{6}}, title = {{{Toward Audience-aware Argument Generation}}}, volume = {{2}}, year = {{2021}}, } @inproceedings{25297, author = {{Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 8th Workshop on Argument Mining}}, pages = {{184 -- 189}}, title = {{{Key Point Analysis via Contrastive Learning and Extractive Argument Summarization}}}, year = {{2021}}, } @inproceedings{25294, author = {{Nouri, Zahra and Prakash, Nikhil and Gadiraju, Ujwal and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021}}, title = {{{iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing}}}, year = {{2021}}, } @inproceedings{25295, author = {{Gurcke, Timon and Alshomary, Milad and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 8th Workshop on Argument Mining}}, pages = {{67 -- 77}}, title = {{{Assessing the Sufficiency of Arguments through Conclusion Generation}}}, year = {{2021}}, } @inproceedings{23709, author = {{Chen, Wei-Fan and Al Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2021}}, pages = {{2683 -- 2693}}, title = {{{Controlled Neural Sentence-Level Reframing of News Articles}}}, year = {{2021}}, }