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Code and Results for "Universals of word order reflect optimization of grammars for efficient communication"

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# Universals of word order reflect optimization of grammars for efficient communication

This repository contains all code and results from the paper.

Code for reproducing statistical analyses and figures is in `results`.
Code for running the neural network models and the control studies reported in SI is in `models`.
Grammar parameters and efficiency scores for all grammars are in `grammars`.

## Requirements
Most analyses only require:
* R: We used version 3.5.1. Analyses require the packages `brms`, `lme4`, `tidyr`, `dplyr`, `ggplot2`.

Creating optimized grammars, or evaluating the efficiency of grammars, requires:

* Python 2.7
* [PyTorch](https://pytorch.org/get-started/locally/), with CUDA. We used PyTorch Version 0.4.1 for experiments, though the code is compatible with more recent versions.
* Extracting real grammars from actual orderings found in corpora additionally requires [Pyro](https://pyro.ai/).
* For the Universal Dependencies corpus data, see `models/corpus_reader/README.md` for instructions.

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