CMU PhD StudentHi! I'm Joel, a PhD student in the Language Technologies Institute at Carnegie Mellon University, advised by Maarten Sap.
My research spans natural language processing, computational social science, and cultural analytics.
Recent projects include:
- Modeling social and structural aspects of narrative discourse in social media
- Evaluating robustness of deep learning models to linguistic variation, such as dialect
- Examining how people conceptualize language models and negotiate boundaries for acceptable use and personalization
Currently, I'm exploring computational models of textual interpretation, with particular focus on contextual reasoning and variation across individuals and groups.
I have an M.S. in Language Technologies from CMU and a B.S. in Computer Science and English from Duke University, where I was advised by Aarthi Vadde. I also previously worked as a software engineer at Amazon Web Services.
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Carnegie Mellon UniversityPhD Student in Language and Information Technologies8/25 - Present -
Carnegie Mellon UniversityM.S. in Language Technologies8/23 - 8/25
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Duke UniversityB.S. in Computer Science and English8/16 - 5/20 -
University of OxfordVisiting Student8/18 - 12/18
Experience
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Amazon Web ServicesSoftware Developer EngineerI (8/20 - 3/23), II (4/23 - 8/23)
News
Selected Publications (view all )
Rejected Dialects: Biases Against African American Language in Reward Models
Joel Mire*, Zubin Trivadi Aysola*, Daniel Chechelnitsky, Nicholas Deas, Chrysoula Zerva, Maarten Sap (* equal contribution)
NAACL (Findings) 2025
We introduce a framework for evaluating dialect biases in reward models and conduct a case study showing biases against African American Language texts.
Rejected Dialects: Biases Against African American Language in Reward Models
Joel Mire*, Zubin Trivadi Aysola*, Daniel Chechelnitsky, Nicholas Deas, Chrysoula Zerva, Maarten Sap (* equal contribution)
NAACL (Findings) 2025
We introduce a framework for evaluating dialect biases in reward models and conduct a case study showing biases against African American Language texts.
HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs
Jocelyn Shen, Joel Mire, Hae Won Park, Cynthia Breazeal, Maarten Sap
EMNLP 2024
We propose a theoretical framework of narrative style as it relates to empathy, and use LLMs to quantify narrative style for downstream social and behavioral insights.
HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs
Jocelyn Shen, Joel Mire, Hae Won Park, Cynthia Breazeal, Maarten Sap
EMNLP 2024
We propose a theoretical framework of narrative style as it relates to empathy, and use LLMs to quantify narrative style for downstream social and behavioral insights.
The Empirical Variability of Narrative Perceptions of Social Media Texts
Joel Mire, Maria Antoniak, Elliott Ash, Andrew Piper, Maarten Sap
EMNLP 2024
We present a dataset and taxonomy of crowd workers' descriptive perceptions of storytelling, analyzing patterns of disagreement among them and across other annotation contexts, including prescriptive labels from researchers and predictions from LLMs.
The Empirical Variability of Narrative Perceptions of Social Media Texts
Joel Mire, Maria Antoniak, Elliott Ash, Andrew Piper, Maarten Sap
EMNLP 2024
We present a dataset and taxonomy of crowd workers' descriptive perceptions of storytelling, analyzing patterns of disagreement among them and across other annotation contexts, including prescriptive labels from researchers and predictions from LLMs.
Where Do People Tell Stories Online? Story Detection Across Online Communities
Maria Antoniak, Joel Mire, Maarten Sap, Elliott Ash, Andrew Piper
ACL 2024
We develop StorySeeker, a toolkit for detecting stories in online conversations, and use it to illuminate distributional characteristics of storytelling across a community-centric social media platform.
Where Do People Tell Stories Online? Story Detection Across Online Communities
Maria Antoniak, Joel Mire, Maarten Sap, Elliott Ash, Andrew Piper
ACL 2024
We develop StorySeeker, a toolkit for detecting stories in online conversations, and use it to illuminate distributional characteristics of storytelling across a community-centric social media platform.