dair-iitd / PoolingAnalysis

[EMNLP'20][Findings] Official Repository for the paper "Why and when should you pool? Analyzing Pooling in Recurrent Architectures."
https://pratyushmaini.github.io/Pooling-Analysis
8 stars 1 forks source link
analyzing-pooling emnlp2020 lstm positional-bias recurrent-architectures sentiment-classification vanishing-gradient

Analyzing Pooling in Recurrent Architectures

Repository for the paper Analyzing Pooling in Recurrent Architectures by Pratyush Maini, Kolluru Sai Keshav, Danish Pruthi and Mausam

Dependencies

The code requires the following dependencies to run can be installed using the conda environment file provided:

conda env create --file environment.yaml

Running gradients experiments

Evaluate the Initial gradient distribution

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

Vanishing Ratios

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

Train a model in the Wiki setting

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

Bias Evaluation

Change the test time distribution

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

Get the NWI scores

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

How can I cite this work?

@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",
}