Papers by Joseph Gatto
REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction (2025.findings-emnlp)
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| Challenge: | Existing work evaluates event argument extraction with exact match (EM), where predicted arguments must align exactly with annotated spans. |
| Approach: | They propose a Reliable Evaluation framework for Generative event argument extraction that combines exact, relaxed, and LLM-based matching to better align with human judgment. |
| Outcome: | Experiments on six datasets show that REGen achieves an average performance gain of +23.93 F1 over EM, reflecting capabilities overlooked by prior evaluation. |
How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting (2026.acl-long)
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Parker Seegmiller, Joseph Gatto, Sarah E. Greer, Ganza Belise Isingizwe, Rohan Ray, Timothy E. Burdick, Sarah Masud Preum
| Challenge: | Large language models (LLMs) have been shown to be effective in drafting patient portal responses, yet their integration into clinical workflows raises various concerns. |
| Approach: | They propose a taxonomy of thematic elements in clinician responses and a framework for assessing clinician editing load of LLM-drafted responses at both content and theme levels. |
| Outcome: | The proposed framework assesses the editing load of LLM-drafted responses at both content and theme levels. |
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments (2024.emnlp-main)
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| Challenge: | Existing work on event-specific argument extraction is limited to contiguous spans of text . Existing approaches to event-centric information extraction are limited to explicit arguments . |
| Approach: | They propose two key argument types that cannot be modeled by existing EE frameworks . implicit and scattered arguments are crucial to elicit full breadth of information required for proper event modeling. |
| Outcome: | The proposed dataset includes 7,464 argument annotations from online health discourse. |
Document-Level Event-Argument Data Augmentation for Challenging Role Types (2025.acl-long)
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| Challenge: | Existing methods for Event Argument Extraction (EAE) are not well-suited to a variety of real-world situations, including long documents and challenging role types. |
| Approach: | They propose two novel methods for generating document-level EAE samples using zero in-domain training data and validate their generalizability. |
| Outcome: | The proposed methods show significant performance increases in low-resource settings. |
Follow-up Question Generation For Enhanced Patient-Provider Conversations (2025.acl-long)
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Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Inas S. Khayal, Sarah DeLozier, Sarah Masud Preum
| Challenge: | Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. |
| Approach: | They propose a framework that generates personalized follow-up questions based on patient utterances and prior EHR data. |
| Outcome: | The framework reduces follow-up communications by 34% and improves performance by 17% and 5% on real and synthetic data. |
Chain-of-Thought Embeddings for Stance Detection on Social Media (2023.findings-emnlp)
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| Challenge: | Stance detection on social media platforms like Twitter is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit stance labels. |
| Approach: | They propose to embed COT reasonings into a traditional RoBERTa-based stance detection pipeline by embedding COT stance reasonings and integrating them into slang-based models. |
| Outcome: | The proposed model achieves SOTA performance on multiple stance detection datasets collected from social media. |