Papers by Yuval Kirstain
A Few More Examples May Be Worth Billions of Parameters (2022.findings-emnlp)
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| Challenge: | Recent work on few-shot learning for natural language tasks explores the dynamics of scaling up either the number of model parameters or labeled examples while controlling for the other variable by setting it to a constant. |
| Approach: | They explore the dynamics of scaling up the number of model parameters versus the number labeled examples across a wide variety of tasks. |
| Outcome: | The results show that scaling parameters yields performance improvements, while adding examples does not. |
Few-Shot Question Answering by Pretraining Span Selection (2021.acl-long)
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| Challenge: | Pretraining models with recurring span selection are effective, but perform poorly in a few-shot setting. |
| Approach: | They propose recurring span selection scheme that asks model to select correct span in passage with multiple sets of recurring recurrings. |
| Outcome: | The proposed model achieves 72.7 F1 on multiple benchmarks while maintaining competitive performance in the high-resource setting. |
Coreference Resolution without Span Representations (2021.acl-short)
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| Challenge: | Pretraining has reduced many complex task-specific NLP models to simple lightweight layers. |
| Approach: | They propose a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, pruning heuristics, and more. |
| Outcome: | The proposed model performs competitively with the current standard model, while being simpler and more efficient. |