Dataset Open Access
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
Davis, Forrest;
van Schijndel, Marten
This repository contains the raw results (by word information-theoretic measures for the experimental stimuli) and the LSTM models analyzed in Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment. 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.
The naming convention for each model in the models directory is:
[Language]_hidden[Hidden Units]_batch[Batch Size]_dropout[Dropout Rate]_lr[Learning Rate]_[Model Number].pt
Language: en for English and es for Spanish
Hidden Units: All models had two layers with 650 hidden units per layer
Batch Size: The size of the batch (128 for English, 64 for Spanish)
Dropout Rate: All models used a dropout rate of 0.2
Learning Rate: All models has a learning rate of 20
Model Number: Identifier of the model (English model 0 is the best model from Gulordava et al. (2018))
| Name | Size | |
|---|---|---|
|
models.tar.gz
md5:01ef23e1a95175f01bc32c55be3513fd |
1.6 GB | Download |
|
raw_results.tar.gz
md5:c62dc12445cbed42a4aaad3d02254632 |
380.3 MB | Download |
|
synthetic.tar.gz
md5:29d2366dab3c2b2c1194cadfdf022b50 |
3.4 GB | Download |
| All versions | This version | |
|---|---|---|
| Views | 70 | 70 |
| Downloads | 19 | 19 |
| Data volume | 28.3 GB | 28.3 GB |
| Unique views | 66 | 66 |
| Unique downloads | 9 | 9 |
- Publication date:
- April 30, 2020
- DOI:
-
- License (for files):
- Creative Commons Attribution 4.0 International