Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal

Benoit Crabbé, Murielle Fabre, Christophe Pallier


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.
Anthology ID:
D19-1106
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1150–1160
URL:
https://www.aclweb.org/anthology/D19-1106
DOI:
10.18653/v1/D19-1106
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https://www.aclweb.org/anthology/D19-1106.pdf
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