Papers by Cees Snoek
Flow Matching for Conditional Text Generation in a Few Sampling Steps (2024.eacl-short)
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| Challenge: | Current diffusion models face multiple drawbacks including slow sampling, noise schedule sensitivity, and misalignment between training and sampling stages. |
| Approach: | They propose a method which leverages flow matching for conditional text generation. |
| Outcome: | The proposed method can generate text in a few steps by training with a novel anchor loss, alleviating the need for expensive hyperparameter optimization of the noise schedule prevalent in diffusion models. |
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation (2021.acl-long)
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| Challenge: | Existing methods for word sense disambiguation (WSD) lack large annotated datasets with sufficient coverage of words . performance of such methods lags behind fully-supervised methods . a meta-learning model is proposed to solve this problem . |
| Approach: | They propose a model of semantic memory for supervised word sense disambiguation using meta-learning. |
| Outcome: | The proposed model improves performance in few-shot WSD and produces meaning prototypes that capture similar senses of distinct words. |