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        <title>Unsupervised Grammar Induction with Depth-bounded PCFG</title>
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        <namePart type="given">Lifeng</namePart>
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    <abstract>There has been recent interest in applying cognitively- or empirically-motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, grammars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00016</identifier>
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        <date>2018</date>
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            <start>211</start>
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%0 Journal Article
%T Unsupervised Grammar Induction with Depth-bounded PCFG
%A Jin, Lifeng
%A Doshi-Velez, Finale
%A Miller, Timothy
%A Schuler, William
%A Schwartz, Lane
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F jin-etal-2018-unsupervised
%X There has been recent interest in applying cognitively- or empirically-motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence models, and therefore more fully exploits the space reductions of depth-bounding. Results for this model on grammar acquisition from transcribed child-directed speech and newswire text exceed or are competitive with those of other models when evaluated on parse accuracy. Moreover, grammars acquired from this model demonstrate a consistent use of category labels, something which has not been demonstrated by other acquisition models.
%R 10.1162/tacl_a_00016
%U https://aclanthology.org/Q18-1016
%U https://doi.org/10.1162/tacl_a_00016
%P 211-224

Markdown (Informal)

[Unsupervised Grammar Induction with Depth-bounded PCFG](https://aclanthology.org/Q18-1016) (Jin et al., TACL 2018)

• Unsupervised Grammar Induction with Depth-bounded PCFG (Jin et al., TACL 2018)

ACL

• Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, and Lane Schwartz. 2018. Unsupervised Grammar Induction with Depth-bounded PCFG. Transactions of the Association for Computational Linguistics, 6:211–224.