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Perspektivenwechsel. Bildinformationen anzeigen

Perspektivenwechsel.

Foto: Universität Paderborn

Milad Alshomary

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Profil
Publikationen
 Milad Alshomary

Social Media in soziotechnischen Systemen

Mitglied - Wissenschaftlicher Mitarbeiter - Research Assistant

Sonderforschungsbereich 901

Mitglied - Wissenschaftlicher Mitarbeiter

Telefon:
+49 5251 60-6587
Büro:
ZM2.A.3.03
General

Hi! I am a PhD student and research assistant in the department of Computational Social Science in Paderborn University

Research Interests
  • Argument Mining and Generation
  • Deep Learning for NLP

Liste im Research Information System öffnen

2021

Generating Informative Conclusions for Argumentative Texts

S. Syed, K. Al-Khatib, M. Alshomary, H. Wachsmuth, M. Potthast, 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


Toward Audience-aware Argument Generation

M. Alshomary, H. Wachsmuth, Patterns (2021), 2(6)


Argument Undermining: Counter-Argument Generation by Attacking Weak Premises

M. Alshomary, S. Syed, M. Potthast, H. Wachsmuth, 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


Belief-based Generation of Argumentative Claims

M. Alshomary, W. Chen, T. Gurcke, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics, 2021, pp. 224-223

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.


    Counter-Argument Generation by Attacking Weak Premises

    M. Alshomary, S. Syed, A. Dhar, M. Potthast, H. Wachsmuth, 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


    2020

    Target Inference in Argument Conclusion Generation

    M. Alshomary, S. Syed, M. Potthast, H. Wachsmuth, in: Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Association for Computational Linguistics, 2020, pp. 4334-4345


    Extractive Snippet Generation for Arguments

    M. Alshomary, N. Düsterhus, H. Wachsmuth, in: Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 1969-1972


    2019

    Modeling Frames in Argumentation

    Y. Ajjour, M. Alshomary, H. Wachsmuth, B. Stein, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, 2019, pp. 2915 - 2925


    Wikipedia Text Reuse: Within and Without

    M. Alshomary, M. Völske, T. Licht, H. Wachsmuth, B. Stein, M. Hagen, M. Potthast, in: Advances in Information Retrieval, Springer International Publishing, 2019, pp. 747-754

    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

      Reproducible Web Corpora

      J. Kiesel, F. Kneist, M. Alshomary, B. Stein, M. Hagen, M. Potthast, Journal of Data and Information Quality (2018), pp. 1-25


      2017

      Webis at the CLEF 2017 Dynamic Search Lab

      M. Hagen, J. Kiesel, M. Alshomary, B. Stein, in: Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum, 2017


      2014

      iSoNTRE: The Social Network Transformer into Recommendation Engine

      C. Abu Quba Rana, S. Hassas, F. Usama, M. Alshomary, C. Gertosio, 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA) (2014), pp. 169-175


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