Evaluating Transformer's Ability to Learn Mildly Context-Sensitive Languages

• URL: http://arxiv.org/abs/2309.00857v2
• Date: Thu, 19 Oct 2023 11:41:58 GMT
• Title: Evaluating Transformer's Ability to Learn Mildly Context-Sensitive Languages
• Authors: Shunjie Wang, Shane Steinert-Threlkeld
• Abstract summary: Recent studies suggest that self-attention is theoretically limited in learning even some regular and context-free languages. We test the Transformer's ability to learn mildly context-sensitive languages of varying complexities. Our analyses show that the learned self-attention patterns and representations modeled dependency relations and demonstrated counting behavior.
• Score: 6.227678387562755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the fact that Transformers perform well in NLP tasks, recent studies suggest that self-attention is theoretically limited in learning even some regular and context-free languages. These findings motivated us to think about their implications in modeling natural language, which is hypothesized to be mildly context-sensitive. We test the Transformer's ability to learn mildly context-sensitive languages of varying complexities, and find that they generalize well to unseen in-distribution data, but their ability to extrapolate to longer strings is worse than that of LSTMs. Our analyses show that the learned self-attention patterns and representations modeled dependency relations and demonstrated counting behavior, which may have helped the models solve the languages.

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