@inproceedings{gonering-morgan-2020-processing,
title = "Processing effort is a poor predictor of cross-linguistic word order frequency",
author = "Gonering, Brennan and
Morgan, Emily",
booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.conll-1.18",
doi = "10.18653/v1/2020.conll-1.18",
pages = "245--255",
abstract = "Some have argued that word orders which are more difficult to process should be rarer cross-linguistically. Our current study fails to replicate the results of Maurits, Navarro, and Perfors (2010), who used an entropy-based Uniform Information Density (UID) measure to moderately predict the Greenbergian typology of transitive word orders. We additionally report an inability of three measures of processing difficulty {---} entropy-based UID, surprisal-based UID, and pointwise mutual information {---} to correctly predict the correct typological distribution, using transitive constructions from 20 languages in the Universal Dependencies project (version 2.5). However, our conclusions are limited by data sparsity.",
}
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<abstract>Some have argued that word orders which are more difficult to process should be rarer cross-linguistically. Our current study fails to replicate the results of Maurits, Navarro, and Perfors (2010), who used an entropy-based Uniform Information Density (UID) measure to moderately predict the Greenbergian typology of transitive word orders. We additionally report an inability of three measures of processing difficulty — entropy-based UID, surprisal-based UID, and pointwise mutual information — to correctly predict the correct typological distribution, using transitive constructions from 20 languages in the Universal Dependencies project (version 2.5). However, our conclusions are limited by data sparsity.</abstract>
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%0 Conference Proceedings
%T Processing effort is a poor predictor of cross-linguistic word order frequency
%A Gonering, Brennan
%A Morgan, Emily
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F gonering-morgan-2020-processing
%X Some have argued that word orders which are more difficult to process should be rarer cross-linguistically. Our current study fails to replicate the results of Maurits, Navarro, and Perfors (2010), who used an entropy-based Uniform Information Density (UID) measure to moderately predict the Greenbergian typology of transitive word orders. We additionally report an inability of three measures of processing difficulty — entropy-based UID, surprisal-based UID, and pointwise mutual information — to correctly predict the correct typological distribution, using transitive constructions from 20 languages in the Universal Dependencies project (version 2.5). However, our conclusions are limited by data sparsity.
%R 10.18653/v1/2020.conll-1.18
%U https://aclanthology.org/2020.conll-1.18
%U https://doi.org/10.18653/v1/2020.conll-1.18
%P 245-255
Markdown (Informal)
[Processing effort is a poor predictor of cross-linguistic word order frequency](https://aclanthology.org/2020.conll-1.18) (Gonering & Morgan, CoNLL 2020)
ACL