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Sunny start to the new semester (April 2023). Show image information

Sunny start to the new semester (April 2023).

Photo: Paderborn University, Besim Mazhiqi

Milad Alshomary

Contact
Profile
Publications
 Milad Alshomary

Transregional Collaborative Research Centre 318

Member - Research Student - Research Assistant

Computational Social Science

Member - Research Associate - Research Assistant

Phone:
+49 5251 60-6587
Office:
ZM2.A.3.03
Visitor:
Zukunftsmeile 2
33102 Paderborn
General

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

Research Interests

Research Interest:

- Argument Mining and Generation.

- Deep Learning for NLP


Open list in Research Information System

2022

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

M. Alshomary, R. El Baff, T. Gurcke, H. Wachsmuth, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 8782 - 8797


"Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain

H. Wachsmuth, M. Alshomary, in: Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 344 - 354


Identifying the Human Values behind Arguments

J. Kiesel, M. Alshomary, N. Handke, X. Cai, H. Wachsmuth, B. Stein, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4459 - 4471


Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning

M. Sengupta, M. Alshomary, H. Wachsmuth, in: Proceedings of the 2022 Workshop on Figurative Language Processing, 2022


Argument Novelty and Validity Assessment via Multitask and Transfer Learning

M. Alshomary, M. Stahl, in: Proceedings of the 9th Workshop on Argument Mining, International Conference on Computational Linguistics, 2022, pp. 111–114

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.


Generating Contrastive Snippets for Argument Search

M. Alshomary, J. Rieskamp, H. Wachsmuth, in: Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21 - 31

DOI


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, pp. 3482-3493


Toward Audience-aware Argument Generation

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


Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

M. Alshomary, T. Gurcke, S. Syed, P. Heinisch, M. Spliethöver, P. Cimiano, M. Potthast, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 184 - 189


Assessing the Sufficiency of Arguments through Conclusion Generation

T. Gurcke, M. Alshomary, H. Wachsmuth, in: Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 67 - 77


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.


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), Association for Computational Linguistics, 2021, pp. 1816–1827

DOI


2020

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


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


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

DOI


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


Open list in Research Information System

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