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 can be trained efficiently to reach relatively high performance%3B however%2C they have trouble with inference efficiency due to the explicit generation of syntactic structures. In this paper%2C we propose a new method dubbed tree-planting: instead of explicitly generating syntactic structures%2C we plant trees into attention weights of unidirectional Transformer LMs to implicitly reflect syntactic structures of natural language. Specifically%2C unidirectional Transformer LMs trained with tree-planting will be called Tree-Planted Transformers (TPT)%2C which inherit the training efficiency from SLMs without changing the inference efficiency of their underlying Transformer LMs. Targeted syntactic evaluations on the SyntaxGym benchmark demonstrated that TPTs%2C despite the lack of explicit generation of syntactic structures%2C significantly outperformed not only vanilla Transformer LMs but also various SLMs that generate hundreds of syntactic structures in parallel. This result suggests that TPTs can learn human-like syntactic knowledge as data-efficiently as SLMs while maintaining the modeling space of Transformer LMs unchanged.?quality=80&w=800)
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