Papers by Hannah Recknor
Grounding Partially-Defined Events in Multimodal Data (2024.findings-emnlp)
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Kate Sanders, Reno Kriz, David Etter, Hannah Recknor, Alexander Martin, Cameron Carpenter, Jingyang Lin, Benjamin Van Durme
| Challenge: | Evidence suggests prelinguistic infants are capable of recognizing discrete events in real-world stimuli. |
| Approach: | They propose a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. |
| Outcome: | The proposed approach can extract events from 14.5 hours of annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. |
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers? (2025.findings-emnlp)
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Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, Benjamin Van Durme
| Challenge: | CLAIMCHECK is an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews from OpenReview. |
| Approach: | They annotate NeurIPS 2023 and 2024 submissions and reviews for weaknesses and dispute them for fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
| Outcome: | The proposed dataset is richly annotated by ML experts for weaknesses statements in the reviews and the claims that they dispute, as well as fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
WikiVideo: Article Generation from Multiple Videos (2026.findings-acl)
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Alexander Martin, Reno Kriz, William Gantt Walden, Kate Sanders, Hannah Recknor, Eugene Yang, Francis Ferraro, Benjamin Van Durme
| Challenge: | Existing methods for retrieval-augmented generation focus on text rather than video. |
| Approach: | They propose a benchmark to generate Wikipedia-style articles from multiple videos . they propose 'collaborative article generation' that leverages an r1-style reasoning model and a VideoLLM to draw higher-level inferences about the target event than is possible with VideoLLms alone. |
| Outcome: | The proposed method outperforms existing methods in oracle retrieval and RAG settings while suggesting promising avenues for future work. |