How Can Self-Attention Networks Recognize Dyck-n Languages?
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We focus on the recognition of Dyck-n (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 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 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.
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