SondreWold / EquiScan

Implementing a transformer model for the SCAN compositionality tasks.

On the Compositional Skills of Sequence-to-Sequence Transformers

This repository implements a standard encoder-decoder transformer model for the SCAN task, as put forth in Brendan Lake & Marco Beroni, 2018

Data

The data can be downloaded by running: chmod u+x ./download_data.sh ./download_data.sh

Implementation

Models were tuned with an informal hyperparameter search on the "simple" task. In the paper, it seem like they use a batch size of 1 for training, so this is also the default in this repository. The best configuration was:

Hyperparameter	Value
lr	0.0003
layers	2
hidden_size	128
attention_heads	2
epochs	5

This gives a model of approximately 850 000 parameters, roughly the same as the LSTM models used in the paper. The models could easily run on CPU on the Apple M1.

Results

Results of the "simple" task for different dataset sizes..

For the length productivity task, the model got a 82.27% accuracy.