Controllable Simplification via Operation Classification

This repo contains code and data used to run experiments presented in the NAACL 2022 paper, Controllable Sentence Simplification via Operation Classification.

IRSD data

The IRSD dataset is available in data/. The publicly available version does not include any examples from Newsela-auto. To access the Newsela-auto examples, please obtain a licence from Newsela before contacting the authors directly.

Pretrained models

Pretrained PyTorch models for the 4-class operation classifier and controllable simplification model are available on HuggingFace.

To use the classifier:

from transformers import RobertaForSequenceClassification, AutoTokenizer

model = RobertaForSequenceClassification.from_pretrained("liamcripwell/ctrl44-clf")
tokenizer = AutoTokenizer.from_pretrained("liamcripwell/ctrl44-clf")

text = "Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
  logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
predicted_class_name = model.config.id2label[predicted_class_id]

To use the controllable simplification model:

from transformers import BartForConditionalGeneration, AutoTokenizer

model = BartForConditionalGeneration.from_pretrained("liamcripwell/ctrl44-simp")
tokenizer = AutoTokenizer.from_pretrained("liamcripwell/ctrl44-simp")

text = "<para> Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017."
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, num_beams=10, max_length=128)

Training models

The following is an example of how to train a RoBERTa-based operation classifier:

doc_simp/models/train.py --data_file=<train_file.csv> --val_file=<val_file.csv> --learning_rate=3e-5 --x_col=complex --y_col=label --batch_size=32 --arch=classifier --model_type=roberta

Generative models can be trained as follows:

python control_simp/models/train.py --arch=bart --data_file=<train_file.csv> --val_file=<val_file.csv> --learning_rate=3e-5 --batch_size=16 --max_source_length=128 --max_target_length=128 --eval_beams=4 --x_col=complex --y_col=simple 

See the source code for additional arguments.

Evaluating models

Operation classifiers can be run from the terminal as follows (test_file should be a .csv):

python control_simp/models/eval.py clf <model_ckpt> <test_file> <out_file> --input_col=<sentence_col>

Generative models can be used for inference/evaluation in a similar fashion. For pipeline models, operation control tokens are dictated from a column in the input data specified via the --ctrl_toks flag. Disabling SAMSA with --samsa=False dramatically reduces runtime.

# End-to-End Model
python control_simp/models/eval.py bart <model_ckpt> <test_file> <output_dir> <run_name> --samsa=False

# CTRL Model
python control_simp/models/eval.py bart <model_ckpt> <test_file> <output_dir> <run_name> --ctrl_toks=<label_col> --samsa=False

Citation

If you find this repository useful, please cite our publication Controllable Sentence Simplification via Operation Classification.

@inproceedings{cripwell-etal-2022-controllable,
    title = "Controllable Sentence Simplification via Operation Classification",
    author = {Cripwell, Liam  and
      Legrand, Jo{\"e}l  and
      Gardent, Claire},
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-naacl.161",
    pages = "2091--2103",
}