@inproceedings{gonering-morgan-2020-processing,
title = "Processing effort is a poor predictor of cross-linguistic word order frequency",
author = "Gonering, Brennan and
Morgan, Emily",
editor = "Fern{\'a}ndez, Raquel and
Linzen, Tal",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gonering-morgan-2020-processing">
<titleInfo>
<title>Processing effort is a poor predictor of cross-linguistic word order frequency</title>
</titleInfo>
<name type="personal">
<namePart type="given">Brennan</namePart>
<namePart type="family">Gonering</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emily</namePart>
<namePart type="family">Morgan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 24th Conference on Computational Natural Language Learning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Raquel</namePart>
<namePart type="family">Fernández</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tal</namePart>
<namePart type="family">Linzen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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>
<identifier type="citekey">gonering-morgan-2020-processing</identifier>
<identifier type="doi">10.18653/v1/2020.conll-1.18</identifier>
<location>
<url>https://aclanthology.org/2020.conll-1.18</url>
</location>
<part>
<date>2020-11</date>
<extent unit="page">
<start>245</start>
<end>255</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Processing effort is a poor predictor of cross-linguistic word order frequency
%A Gonering, Brennan
%A Morgan, Emily
%Y Fernández, Raquel
%Y Linzen, Tal
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%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