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        <title>Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal</title>
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        <namePart type="given">Benoit</namePart>
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        <namePart type="given">Murielle</namePart>
        <namePart type="family">Fabre</namePart>
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    <name type="personal">
        <namePart type="given">Christophe</namePart>
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            <namePart type="given">Kentaro</namePart>
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        <name type="personal">
            <namePart type="given">Jing</namePart>
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            <namePart type="given">Xiaojun</namePart>
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    <abstract>This paper describes a method of variable beam size inference for Recurrent Neural Network Grammar (rnng) by drawing inspiration from sequential Monte-Carlo methods such as particle filtering. The paper studies the relevance of such methods for speeding up the computations of direct generative parsing for rnng. But it also studies the potential cognitive interpretation of the underlying representations built by the search method (beam activity) through analysis of neuro-imaging signal.</abstract>
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%0 Conference Proceedings
%T Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal
%A Crabbé, Benoit
%A Fabre, Murielle
%A Pallier, Christophe
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F crabbe-etal-2019-variable
%X This paper describes a method of variable beam size inference for Recurrent Neural Network Grammar (rnng) by drawing inspiration from sequential Monte-Carlo methods such as particle filtering. The paper studies the relevance of such methods for speeding up the computations of direct generative parsing for rnng. But it also studies the potential cognitive interpretation of the underlying representations built by the search method (beam activity) through analysis of neuro-imaging signal.
%R 10.18653/v1/D19-1106
%U https://aclanthology.org/D19-1106
%U https://doi.org/10.18653/v1/D19-1106
%P 1150-1160

Markdown (Informal)

[Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal](https://aclanthology.org/D19-1106) (Crabbé et al., EMNLP-IJCNLP 2019)

• Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal (Crabbé et al., EMNLP-IJCNLP 2019)

ACL

• Benoit Crabbé, Murielle Fabre, and Christophe Pallier. 2019. Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1150–1160, Hong Kong, China. Association for Computational Linguistics.