Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing

Jean Maillard; Stephen Clark

doi:10.18653/v1/w18-2903

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arXiv · 1806.00840

Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing

Jean Maillard·Stephen Clark

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Abstract

Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.

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Jean Maillard, Stephen Clark. 2018-06-03. Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing. https://doi.org/10.18653/v1%2Fw18-2903

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