@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{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}}, } @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{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{34051, abstract = {{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.}}, author = {{Alshomary, Milad and Stahl, Maja}}, booktitle = {{Proceedings of the 9th Workshop on Argument Mining}}, pages = {{111–114}}, publisher = {{International Conference on Computational Linguistics}}, title = {{{Argument Novelty and Validity Assessment via Multitask and Transfer 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{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}}, } @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{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{21178, abstract = {{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.}}, author = {{Alshomary, Milad and Chen, Wei-Fan and Gurcke, Timon and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}}, location = {{Online}}, pages = {{224--223}}, publisher = {{Association for Computational Linguistics}}, title = {{{Belief-based Generation of Argumentative Claims}}}, year = {{2021}}, } @inproceedings{22229, author = {{Alshomary, Milad and Syed, Shahbaz and Potthast, Martin 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)}}, location = {{Online}}, pages = {{1816–1827}}, publisher = {{Association for Computational Linguistics}}, title = {{{Argument Undermining: Counter-Argument Generation by Attacking Weak Premises}}}, doi = {{10.18653/v1/2021.findings-acl.159}}, year = {{2021}}, } @inproceedings{7283, author = {{Alshomary, Milad and Düsterhus, Nick and Wachsmuth, Henning}}, booktitle = {{Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}}, location = {{Xi'an, China}}, pages = {{1969--1972}}, title = {{{Extractive Snippet Generation for Arguments}}}, year = {{2020}}, } @inproceedings{16868, author = {{Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)}}, location = {{Seattle, USA}}, pages = {{4334--4345}}, publisher = {{Association for Computational Linguistics}}, title = {{{Target Inference in Argument Conclusion Generation}}}, year = {{2020}}, } @inproceedings{12931, author = {{Ajjour, Yamen and Alshomary, Milad and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing}}, pages = {{2915 -- 2925}}, title = {{{Modeling Frames in Argumentation}}}, year = {{2019}}, } @inproceedings{10284, abstract = {{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.}}, author = {{Alshomary, Milad and Völske, Michael and Licht, Tristan and Wachsmuth, Henning and Stein, Benno and Hagen, Matthias and Potthast, Martin}}, booktitle = {{Advances in Information Retrieval}}, editor = {{Azzopardi, Leif and Stein, Benno and Fuhr, Norbert and Mayr, Philipp and Hauff, Claudia and Hiemstra, Djoerd}}, isbn = {{978-3-030-15712-8}}, pages = {{747--754}}, publisher = {{Springer International Publishing}}, title = {{{Wikipedia Text Reuse: Within and Without}}}, year = {{2019}}, } @article{10331, author = {{Kiesel, Johannes and Kneist, Florian and Alshomary, Milad and Stein, Benno and Hagen, Matthias and Potthast, Martin}}, issn = {{1936-1955}}, journal = {{Journal of Data and Information Quality}}, pages = {{1--25}}, title = {{{Reproducible Web Corpora}}}, doi = {{10.1145/3239574}}, year = {{2018}}, } @inproceedings{3904, author = {{Hagen, Matthias and Kiesel, Johannes and Alshomary, Milad and Stein, Benno}}, booktitle = {{Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum}}, title = {{{Webis at the CLEF 2017 Dynamic Search Lab}}}, year = {{2017}}, } @article{3905, author = {{Abu Quba Rana, Chamsi and Hassas, Salima and Usama, Fayyad and Alshomary, Milad and Gertosio, Christine}}, journal = {{2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)}}, pages = {{169--175}}, title = {{{iSoNTRE: The Social Network Transformer into Recommendation Engine}}}, year = {{2014}}, }