MohammadHossein Rezaei

Email: mhrezaei@arizona.edu

I am a third-year undergraduate student at the University of Arizona UArizona majoring in Computer Science. I am a member of the Computational Language Understanding (CLU) Lab CLU lab advised by Dr. Eduardo Blanco. Previously, I was a research intern at Stanford University advised by Dr. Diyi Yang in the SALT Lab SALT . I have worked on various projects in natural language processing and machine learning such as negation understanding and multilingual QA. Currently, I am working on social norms understanding and generalization in language models.

Outside research, I am a Course Coordinator (Head TA) for the CSc 144: Discrete Math for CS I course offered by the computer science department. I also serve as a Peer Mentor and a CS Ambassador for the department to help incoming students and promote computer science education. I am honored to have been awarded the Iranian Scholarship Foundation (ISF) undergraduate scholarship. In my free time, I enjoy playing the piano, listening to music, and hiking.

news

Aug 20, 2024	I participated in the LINXS Summer Research Program at Stanford University in the summer of 2024 as an undergraduate visiting research intern. I was advised by Dr. Diyi Yang in the SALT Lab.
May 16, 2024	My paper, Paraphrasing in Affirmative Terms Improves Negation Understanding, has been accepted to ACL 2024.
Dec 06, 2023	Our paper on Interpreting Indirect Answers to Yes-No Questions in Multiple Languages has been accepted to EMNLP Findings 2023.
Jul 01, 2023	I participated in SoNIC Summer Research Workshop 2023 at Cornell University.

selected publications

ACL
Paraphrasing in Affirmative Terms Improves Negation Understanding

MohammadHossein Rezaei, and Eduardo Blanco

In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Aug 2024

Abs DOI arXiv Bib PDF Poster Slides

Negation is a common linguistic phenomenon. Yet language models face challenges with negation in many natural language understanding tasks such as question answering and natural language inference. In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i.e., paraphrases without negation) to make models more robust against negation. Crucially, our affirmative interpretations are obtained automatically. We show improvements with CondaQA, a large corpus requiring reasoning with negation, and five natural language understanding tasks.
@inproceedings{rezaei-blanco-2024-paraphrasing, title = {Paraphrasing in Affirmative Terms Improves Negation Understanding}, author = {Rezaei, MohammadHossein and Blanco, Eduardo}, editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek}, booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, month = aug, year = {2024}, address = {Bangkok, Thailand}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2024.acl-short.55/}, doi = {10.18653/v1/2024.acl-short.55}, pages = {602--615}, }