gregbugaj/efficient-longdoc-classification

Source codes for ``Efficient Classification of Long Documents Using Transformers''

Please refer to our paper for more details and cite our paper if you find this repo useful:

@inproceedings{park-etal-2022-efficient,
    title = "Efficient Classification of Long Documents Using Transformers",
    author = "Park, Hyunji  and
      Vyas, Yogarshi  and
      Shah, Kashif",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-short.79",
    doi = "10.18653/v1/2022.acl-short.79",
    pages = "702--709",
}

Instructions

1. Install required libraries

pip install -r requirements.txt
python -m spacy download en_core_web_sm

2. Prepare the datasets

Hyperpartisan News Detection

• Available at https://zenodo.org/record/1489920#.YLferh1Olc8
• Download the datasets

mkdir data/hyperpartisan
wget -P data/hyperpartisan/ https://zenodo.org/record/1489920/files/articles-training-byarticle-20181122.zip
wget -P data/hyperpartisan/ https://zenodo.org/record/1489920/files/ground-truth-training-byarticle-20181122.zip
unzip data/hyperpartisan/articles-training-byarticle-20181122.zip -d data/hyperpartisan
unzip data/hyperpartisan/ground-truth-training-byarticle-20181122.zip -d data/hyperpartisan
rm data/hyperpartisan/*zip

• Prepare the datasets with the resulting xml files and this preprocessing script (following Longformer): https://github.com/allenai/longformer/blob/master/scripts/hp_preprocess.py

20NewsGroups

• Originally available at http://qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz
• Running train.py with the --data 20news flag will download and prepare the data available via sklearn.datasets (following CogLTX). We adopt the train/dev/test split from this ToBERT paper.

EURLEX-57K

• Available at https://github.com/iliaschalkidis/lmtc-emnlp2020
• Download the datasets

mkdir data/EURLEX57K
wget -O data/EURLEX57K/datasets.zip http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/datasets.zip
unzip data/EURLEX57K/datasets.zip -d data/EURLEX57K
rm data/EURLEX57K/datasets.zip
rm -rf data/EURLEX57K/__MACOSX
mv data/EURLEX57K/dataset/* data/EURLEX57K
rm -rf data/EURLEX57K/dataset
wget -O data/EURLEX57K/EURLEX57K.json http://nlp.cs.aueb.gr/software_and_datasets/EURLEX57K/eurovoc_en.json

• Running train.py with the --data eurlex flag reads and prepares the data from data/EURLEX57K/{train, dev, test}/*.json files
• Running train.py with the --data eurlex --inverted flag creates Inverted EURLEX data by inverting the order of the sections
• data/EURLEX57K/EURLEX57K.json contains label information.

CMU Book Summary Dataset

• Available at http://www.cs.cmu.edu/~dbamman/booksummaries.html

wget -P data/ http://www.cs.cmu.edu/~dbamman/data/booksummaries.tar.gz
tar -xf data/booksummaries.tar.gz -C data

• Running train.py with the --data books flag reads and prepares the data from data/booksummaries/booksummaries.txt
• Running train.py with the --data books --pairs flag creates Paired Book Summary by combining pairs of summaries and their labels

3. Run the models

e.g. python train.py --model_name bertplusrandom --data books --pairs --batch_size 8 --epochs 20 --lr 3e-05

cf. Note that we use the source code for the CogLTX model: https://github.com/Sleepychord/CogLTX

Hyperparameters used

Hyperpartisan

Parameter	BERT	BERT+TextRank	BERT+Random	Longformer	ToBERT
Batch size	8	8	8	16	8
Epochs	20	20	20	20	20
LR	3e-05	3e-05	5e-05	5e-05	5e-05
Scheduler	NA	NA	NA	warmup	NA

20NewsGroups, Book Summary, Paired Book Summary

Parameter	BERT	BERT+TextRank	BERT+Random	Longformer	ToBERT
Batch size	8	8	8	16	8
Epochs	20	20	20	20	20
LR	3e-05	3e-05	3e-05	0.005	3e-05
Scheduler	NA	NA	NA	warmup	NA

EURLEX, Inverted EURLEX

Parameter	BERT	BERT+TextRank	BERT+Random	Longformer	ToBERT
Batch size	8	8	8	16	8
Epochs	20	20	20	20	20
LR	5e-05	5e-05	5e-05	0.005	5e-05
Scheduler	NA	NA	NA	warmup	NA