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• W3098339586 abstract "We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic programming algorithm that marginalises over latent binary tree structures with N leaves, allowing us to compute the likelihood of a sequence of N tokens under a latent tree model, which we maximise to train a recursive neural function. We demonstrate performance on two synthetic tasks: SCAN, where it outperforms previous models on the LENGTH split, and English question formation, where it performs comparably to decoders with the ground-truth tree structure. We also present experimental results on German-English translation on the Multi30k dataset, and qualitatively analyse the induced tree structures our model learns for the SCAN tasks and the German-English translation task." @default.
• W3098339586 created "2020-11-23" @default.
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• W3098339586 date "2020-01-01" @default.
• W3098339586 modified "2023-10-14" @default.
• W3098339586 title "Recursive Top-Down Production for Sentence Generation with Latent Trees" @default.
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• W3098339586 doi "https://doi.org/10.18653/v1/2020.findings-emnlp.208" @default.
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