| Challenge: | Recent research suggests that transformer-based language models fail to learn basic algorithmic patterns. |
| Approach: | They propose to augment transformer-based language models with a differentiable stack-based attention mechanism that adds a level of interpretability to the model. |
| Outcome: | The proposed model can model some, but not all, deterministic context-freelanguages. |
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| Challenge: | a novel class of Transformer language models that combine expressive power, scalability, and strong performance of Transformers and recursive syntactic compositions. |
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