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.- Anthology ID:
- 2020.conll-1.18
- Volume:
- Proceedings of the 24th Conference on Computational Natural Language Learning
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Raquel Fernández, Tal Linzen
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 245–255
- Language:
- URL:
- https://aclanthology.org/2020.conll-1.18
- DOI:
- 10.18653/v1/2020.conll-1.18
- Cite (ACL):
- Brennan Gonering and Emily Morgan. 2020. Processing effort is a poor predictor of cross-linguistic word order frequency. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 245–255, Online. Association for Computational Linguistics.
- Cite (Informal):
- Processing effort is a poor predictor of cross-linguistic word order frequency (Gonering & Morgan, CoNLL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.conll-1.18.pdf