Repository for the paper Analyzing Pooling in Recurrent Architectures by Pratyush Maini, Kolluru Sai Keshav, Danish Pruthi and Mausam
The code requires the following dependencies to run can be installed using the conda environment file provided:
conda env create --file environment.yaml
python train.py --pool att_max --data_size 20K --gpu_id 0 --mode train --batch_size 1 --task IMDB_LONG --wiki none --epochs 5 --gradients 1 --initial 1 --log 1 --customlstm 1
Results are at model_dir/initial_gradients.txt
python train.py --pool att_max --data_size 20K --gpu_id 0 --mode train --batch_size 32 --task IMDB_LONG --wiki none --epochs 5 --gradients 1 --ratios 1 --log 1 --customlstm 1
Results are at model_dir/ratios.txt
python train.py --pool att_max --data_size 20K --gpu_id 0 --mode train --batch_size 32 --task IMDB_LONG --wiki mid --epochs 20 --log 1 --customlstm 0
Logs are at model_dir/logs.txt
python test.py --pool att_max --data_size 20K --gpu_id 0 --mode test --batch_size 32 --task IMDB_LONG --wiki none --vec 3 --customlstm 0
python test.py --pool att_max --data_size 20K --gpu_id 0 --mode test --batch_size 32 --task IMDB_LONG --wiki none --NWI 1 --customlstm 0
@inproceedings{maini2020pool,
title = "Why and when should you pool? Analyzing Pooling in Recurrent Architectures",
author = "Maini, Pratyush and Kolluru, Keshav and Pruthi, Danish and {Mausam}",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.410",
}