A Transformer with Stack Attention

• URL: http://arxiv.org/abs/2405.04515v2
• Date: Mon, 13 May 2024 18:56:18 GMT
• Title: A Transformer with Stack Attention
• Authors: Jiaoda Li, Jennifer C. White, Mrinmaya Sachan, Ryan Cotterell,
• Abstract summary: We propose augmenting transformer-based language models with a differentiable, stack-based attention mechanism. Our stack-based attention mechanism can be incorporated into any transformer-based language model and adds a level of interpretability to the model. We show that the addition of our stack-based attention mechanism enables the transformer to model some, but not all, deterministic context-free languages.
• Score: 84.18399019794036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural languages are believed to be (mildly) context-sensitive. Despite underpinning remarkably capable large language models, transformers are unable to model many context-free language tasks. In an attempt to address this limitation in the modeling power of transformer-based language models, we propose augmenting them with a differentiable, stack-based attention mechanism. Our stack-based attention mechanism can be incorporated into any transformer-based language model and adds a level of interpretability to the model. We show that the addition of our stack-based attention mechanism enables the transformer to model some, but not all, deterministic context-free languages.

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