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        <title>Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing</title>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/W18-2903</identifier>
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%0 Conference Proceedings
%T Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing
%A Maillard, Jean
%A Clark, Stephen
%S Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP
%D 2018
%8 jul
%I Association for Computational Linguistics
%C Melbourne, Australia
%F maillard-clark-2018-latent
%X 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.
%R 10.18653/v1/W18-2903
%U https://aclanthology.org/W18-2903
%U https://doi.org/10.18653/v1/W18-2903
%P 13-18

Markdown (Informal)

[Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing](https://aclanthology.org/W18-2903) (Maillard & Clark, 2018)

• Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing (Maillard & Clark, 2018)

ACL

• Jean Maillard and Stephen Clark. 2018. Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing. In Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP, pages 13–18, Melbourne, Australia. Association for Computational Linguistics.