Papers by Dylan Ebert
A Visuospatial Dataset for Naturalistic Verb Learning (2020.starsem-1)
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| Challenge: | a new dataset is available for training and evaluating grounded language models . our data is designed to emulate the quality of language data a pre-verbal child would have access to . |
| Approach: | They propose a dataset for training and evaluating grounded language models . they use naturalistic, spontaneous speech paired with richly grounded visuospatial context . |
| Outcome: | The proposed dataset compares two distributional semantics models with one that does not. |
Do Trajectories Encode Verb Meaning? (2022.naacl-main)
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| Challenge: | Distributional models learn representations of words from text but lack grounding or the linking of text to the non-linguistic world. |
| Approach: | They investigate the extent to which trajectories naturally encode verb semantics . they build a procedurally generated agent-object-interaction dataset and compare methods . |
| Outcome: | The proposed model can capture verb semantics by tracing trajectories and self-supervised pretraining. |
Pretraining on Interactions for Learning Grounded Affordance Representations (2022.starsem-1)
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| Challenge: | Existing studies of affordances have not integrated into formal semantics. |
| Approach: | They propose to integrate 3D objects' trajectories into a neural network to predict their traversories. |
| Outcome: | The proposed model outperforms 2D computer vision models and is more accurate than expected. |