How Can Self-Attention Networks Recognize Dyck-n Languages?

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ID: 282326
2020
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Abstract
We focus on the recognition of Dyck-n ($\mathcal{D}_n$) languages with self-attention (SA) networks, which has been deemed to be a difficult task for these networks. We compare the performance of two variants of SA, one with a starting symbol (SA$^+$) and one without (SA$^-$). Our results show that SA$^+$ is able to generalize to longer sequences and deeper dependencies. For $\mathcal{D}_2$, we find that SA$^-$ completely breaks down on long sequences whereas the accuracy of SA$^+$ is 58.82$\%$. We find attention maps learned by $\text{SA}{^+}$ to be amenable to interpretation and compatible with a stack-based language recognizer. Surprisingly, the performance of SA networks is at par with LSTMs, which provides evidence on the ability of SA to learn hierarchies without recursion.
Reference Key
zhang2020how Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Javid Ebrahimi; Dhruv Gelda; Wei Zhang
Journal arXiv
Year 2020
DOI
DOI not found
URL
http://arxiv.org/abs/2010.04303v1
Keywords
cs.lg cs.cl cs.fl

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