Papers by Steven Corman
Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction (2024.naacl-long)
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| Challenge: | Existing methods for document-level argument extraction do not require human involvement and combine uncontextualized and contextualized questions. |
| Approach: | They propose multiple question generation strategies for document-level event argument extraction that do not require human involvement and combine uncontextualized and contextualized questions. |
| Outcome: | The proposed questions do not require human involvement and are suitable for document-level argument extraction. |
UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs’ Memorization (2025.acl-long)
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Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman, Chitta Baral
| Challenge: | UnSeenTimeQA is a data contamination-free time-sensitive question-answering benchmark. |
| Approach: | They propose a data contamination-free time-sensitive question-answering benchmark that avoids web-searchable queries grounded in the real world. |
| Outcome: | The proposed benchmark avoids web-searchable queries grounded in the real world and enables on-demand generation of new samples, mitigating the risk of data leakage. |
BEMEAE: Moving Beyond Exact Span Match for Event Argument Extraction (2025.naacl-long)
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| Challenge: | Event Argument Extraction (EAE) is a complex task that requires deep comprehension of text to accurately identify and classify event arguments. |
| Approach: | They propose a new evaluation metric that integrates deterministic components with a semantic matching component for more accurate assessment. |
| Outcome: | The proposed evaluation metric leads to higher F1 scores and significant changes in model rankings, underscoring ESM’s inadequacy for comprehensive evaluation of EAE. |