Papers by Eric Nalisnick
DefVerify: Do Hate Speech Models Reflect Their Dataset’s Definition? (2025.coling-main)
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| Challenge: | DefVerify is a 3-step procedure that encodes a user-specified definition of hate speech, quantifies to what extent the model reflects the intended definition, and identifies the point of failure in the workflow. |
| Approach: | They propose a 3-step procedure that encodes a user-specified definition of hate speech and quantifies to what extent the model reflects intended definition. |
| Outcome: | The proposed procedure detects gaps between definition and model behavior when applied to six popular hate speech benchmark datasets. |
Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach (2025.emnlp-main)
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| Challenge: | Existing systems for sign language recognition process a signing sequence holistically, leaving handshape information implicit, which limits both recognition accuracy and linguistic analysis. |
| Approach: | They propose a graph neural network that separates temporal dynamics from static handshape configurations in continuous signing sequences. |
| Outcome: | The proposed approach achieves 46% accuracy across 37 handshape classes, compared to 25% for baseline methods. |