Milad Alshomary

Schapiro Center

530 W 120th St

New York, NY 10027

I am a postdoctoral research scientist at the Data Science Institute at Columbia University, with a background in Natural Language Processing (NLP) focusing on human argumentation and the explainability of machine learning models. My research explores the intersection of argumentation and explainability, driven by the increasing need to understand the behavior of AI systems.

I earned my PhD in Computer Science from Paderborn University (July 2018 - December 2023). My doctoral research, resulted in the dissertation titled “Audience-Aware Argument Generation,” under the supervision of Professor Henning Wachsmuth, aimed to advance the effectiveness of argument generation by emphasizing the importance of relevance, consideration of the opponent’s argument, and addressing the audience’s interests. My current postdoctoral research at Columbia University (January 2024 - Present), under the supervision of Professor Kathleen McKeown and Professor Smaranda Muresan, focuses on developing methods for authorship attribution, with a particular emphasis on making these models explainable. This work involves studying how humans explain in dialogues and interpreting latent spaces to understand what aspects of style authorship attribution models capture.

news

Jan 20, 2025	My PhD. disseration recieved the the Dissertation Award 2024 of Paderborn University
Jun 1, 2024	I am co-chairing the publication committee for the (EMNLP-24 conference)
Jan 1, 2024	I started a new position at the Data Science Institute at Columbia University as Postdoctoral Research Scientist.
Dec 28, 2023	I successfully defended my Ph.D. thesis at Paderborn University (tweet). Thesis can be found here (link)
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

Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution

Milad Alshomary, Narutatsu Ri, Marianna Apidianaki, and 3 more authors

arXiv preprint arXiv:2409.07072 2024
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.