Davis, Forrest
van Schijndel, Marten
2020-04-30
<p>This repository contains the raw results (by word information-theoretic measures for the experimental stimuli) and the LSTM models analyzed in <a href="https://www.aclweb.org/anthology/2020.acl-main.179/">Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment</a>. The models from the synthetic experiments are given in the synthetic archive, as well as the training data generation script. There is a README included that gives more details for recreating/evaluating results from those experiments.</p>
<p>The naming convention for each model in the models directory is:<br>
[Language]_hidden[Hidden Units]_batch[Batch Size]_dropout[Dropout Rate]_lr[Learning Rate]_[Model Number].pt</p>
<p>Language: en for English and es for Spanish<br>
Hidden Units: All models had two layers with 650 hidden units per layer<br>
Batch Size: The size of the batch (128 for English, 64 for Spanish)<br>
Dropout Rate: All models used a dropout rate of 0.2<br>
Learning Rate: All models has a learning rate of 20<br>
Model Number: Identifier of the model (English model 0 is the best model from <a href="https://github.com/facebookresearch/colorlessgreenRNNs">Gulordava et al. (2018)</a>) </p>
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https://doi.org/10.5281/zenodo.3778994
oai:zenodo.org:3778994
Zenodo
https://doi.org/10.5281/zenodo.3778993
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
info:eu-repo/semantics/other