Publications

2022

1. The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments

   Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning

   In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics 2022
2. "Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain

   Wachsmuth, Henning and Alshomary, Milad

   In Proceedings of the 29th International Conference on Computational Linguistics 2022
3. Identifying the Human Values behind Arguments

   Kiesel, Johannes and Alshomary, Milad and Handke, Nicolas and Cai, Xiaoni and Wachsmuth, Henning and Stein, Benno

   In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics 2022
4. Generating Contrastive Snippets for Argument Search

   Alshomary, Milad and Rieskamp, Jonas and Wachsmuth, Henning

   In Proceedings of the 9th International Conference on Computational Models of Argument 2022
5. Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning

   Sengupta, Meghdut and Alshomary, Milad and Wachsmuth, Henning

   In Proceedings of the 2022 Workshop on Figurative Language Processing 2022
6. Argument Novelty and Validity Assessment via Multitask and Transfer Learning

   Alshomary, Milad and Stahl, Maja

   In Proceedings of the 9th Workshop on Argument Mining 2022

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   An argument is a constellation of premises reasoning towards a certain conclusion. The automatic generation of conclusions is becoming a very prominent task, raising the need for automatic measures to assess the quality of these generated conclusions. The SharedTask at the 9th Workshop on Argument Mining proposes a new task to assess the novelty and validity of a conclusion given a set of premises. In this paper, we present a multitask learning approach that transfers the knowledge learned from the natural language inference task to the tasks at hand. Evaluation results indicate the importance of both knowledge transfer and joint learning, placing our approach in the fifth place with strong results compared to baselines.

2021

1. Generating Informative Conclusions for Argumentative Texts

   Syed, Shahbaz and Al-Khatib, Khalid and Alshomary, Milad and Wachsmuth, Henning and Potthast, Martin

   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
2. Toward Audience-aware Argument Generation

   Alshomary, Milad and Wachsmuth, Henning

   Patterns 2021
3. Assessing the Sufficiency of Arguments through Conclusion Generation

   Gurcke, Timon and Alshomary, Milad and Wachsmuth, Henning

   In Proceedings of the 8th Workshop on Argument Mining 2021
4. Belief-based Generation of Argumentative Claims

   Alshomary, Milad and Chen, Wei-Fan and Gurcke, Timon and Wachsmuth, Henning

   In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume 2021

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   When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people’s prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we additionally evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users’ beliefs based on their stances, but demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.

5. Argument Undermining: Counter-Argument Generation by Attacking Weak Premises

   Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning

   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
6. Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

   Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning

   In Proceedings of the 8th Workshop on Argument Mining 2021

2020

1. Extractive Snippet Generation for Arguments

   Alshomary, Milad and Düsterhus, Nick and Wachsmuth, Henning

   In Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 2020
2. Target Inference in Argument Conclusion Generation

   Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning

   In Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020) 2020

2019

1. Modeling Frames in Argumentation

   Ajjour, Yamen and Alshomary, Milad and Wachsmuth, Henning and Stein, Benno

   In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing 2019
2. Wikipedia Text Reuse: Within and Without

   Alshomary, Milad and Völske, Michael and Licht, Tristan and Wachsmuth, Henning and Stein, Benno and Hagen, Matthias and Potthast, Martin

   In Advances in Information Retrieval 2019

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   We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample of the Common Crawl). To discover reuse beyond verbatim copy and paste, we employ state-of-the-art text reuse detection technology, scaling it for the first time to process the entire Wikipedia as part of a distributed retrieval pipeline. We further report on a pilot analysis of the 100 million reuse cases inside, and the 1.6 million reuse cases outside Wikipedia that we discovered. Text reuse inside Wikipedia gives rise to new tasks such as article template induction, fixing quality flaws, or complementing Wikipedia’s ontology. Text reuse outside Wikipedia yields a tangible metric for the emerging field of quantifying Wikipedia’s influence on the web. To foster future research into these tasks, and for reproducibility’s sake, the Wikipedia text reuse corpus and the retrieval pipeline are made freely available.

2018

1. Reproducible Web Corpora

   Kiesel, Johannes and Kneist, Florian and Alshomary, Milad and Stein, Benno and Hagen, Matthias and Potthast, Martin

   Journal of Data and Information Quality 2018

2017

1. Webis at the CLEF 2017 Dynamic Search Lab

   Hagen, Matthias and Kiesel, Johannes and Alshomary, Milad and Stein, Benno

   In Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum 2017