Self-Attention Networks Can Process Bounded Hierarchical Languages

05/24/2021

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by   Shunyu Yao, et al.

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Despite their impressive performance in NLP, self-attention networks were recently proved to be limited for processing formal languages with hierarchical structure, such as 𝖣𝗒𝖼𝗄_k, the language consisting of well-nested parentheses of k types. This suggested that natural language can be approximated well with models that are too weak for formal languages, or that the role of hierarchy and recursion in natural language might be limited. We qualify this implication by proving that self-attention networks can process 𝖣𝗒𝖼𝗄_k, D, the subset of 𝖣𝗒𝖼𝗄_k with depth bounded by D, which arguably better captures the bounded hierarchical structure of natural language. Specifically, we construct a hard-attention network with D+1 layers and O(log k) memory size (per token per layer) that recognizes 𝖣𝗒𝖼𝗄_k, D, and a soft-attention network with two layers and O(log k) memory size that generates 𝖣𝗒𝖼𝗄_k, D. Experiments show that self-attention networks trained on 𝖣𝗒𝖼𝗄_k, D generalize to longer inputs with near-perfect accuracy, and also verify the theoretical memory advantage of self-attention networks over recurrent networks.