Milad Alshomary

Schapiro Center

530 W 120th St

New York, NY 10027

My name is Milad Alshomary, a Postdoctoral Research Scientist at the Data Science Institute at Columbia University. I am working with Professor Kathleen McKeown and Professor Smaranda Muresan on author attribution and obfuscation and how to enable ML models to explain their decisions on these tasks. Before joining Columbia, I was a Ph.D. student in the Institute of Artificial Intelligence at Leibniz University Hannover, under the supervision of Henning Wachsmuth, where the focus of my research was on computational argumentation. My publications are on topics like the generation of argument conclusions and summaries, the generation of counter-arguments, and the tuning of argumentative content toward specific audiences.

news

Nov 28, 2023	I successfully defended my Ph.D. thesis at Paderborn University (tweet)
Jan 1, 2023	I am co-organizing the 10th workshop on Argument Mining (link)
Oct 23, 2022	I participated in the “Towards a Unified Model of Scholarly Argumentation” Seminar at Dagtsuhl (link)
Oct 12, 2022	Recently, I attended the COLING-22 conference to present our paper “A Dialogue Corpus for Learning to Construct Explanations” (link)
Aug 1, 2022	We are organizing a SharedTask on Identifying human values in argumentative texts at SemEval 2023 (Link)

selected publications

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
Toward Audience-aware Argument Generation

Alshomary, Milad and Wachsmuth, Henning

Patterns 2021
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.