GECToR – Grammatical Error Correction: Tag, Not Rewrite

This repository provides code for training and testing state-of-the-art models for grammatical error correction with the official PyTorch implementation of the following paper:

GECToR – Grammatical Error Correction: Tag, Not Rewrite
Kostiantyn Omelianchuk, Vitaliy Atrasevych, Artem Chernodub, Oleksandr Skurzhanskyi
Grammarly
15th Workshop on Innovative Use of NLP for Building Educational Applications (co-located with ACL 2020)

It is mainly based on AllenNLP and transformers.

Installation

The following command installs all necessary packages:

pip install -r requirements.txt

The project was tested using Python 3.7.

Datasets

All the public GEC datasets used in the paper can be downloaded from here.
Synthetically created datasets can be generated/downloaded here.
To train the model data has to be preprocessed and converted to special format with the command:

python utils/preprocess_data.py -s SOURCE -t TARGET -o OUTPUT_FILE

Pretrained models

Pretrained encoder	Confidence bias	Min error prob	CoNNL-2014 (test)	BEA-2019 (test)
BERT [link]	0.10	0.41	63.0	67.6
RoBERTa [link]	0.20	0.50	64.0	71.5
XLNet [link]	0.35	0.66	65.3	72.4
RoBERTa + XLNet	0.24	0.45	66.0	73.7
BERT + RoBERTa + XLNet	0.16	0.40	66.5	73.6

Train model

To train the model, simply run:

python train.py --train_set TRAIN_SET --dev_set DEV_SET \
                --model_dir MODEL_DIR

There are a lot of parameters to specify among them:

• cold_steps_count the number of epochs where we train only last linear layer
• transformer_model {bert,distilbert,gpt2,roberta,transformerxl,xlnet,albert} model encoder
• tn_prob probability of getting sentences with no errors; helps to balance precision/recall
• pieces_per_token maximum number of subwords per token; helps not to get CUDA out of memory

In our experiments we had 98/2 train/dev split.

Training parameters

We described all parameters that we use for training and evaluating here.

Model inference

To run your model on the input file use the following command:

python predict.py --model_path MODEL_PATH [MODEL_PATH ...] \
                  --vocab_path VOCAB_PATH --input_file INPUT_FILE \
                  --output_file OUTPUT_FILE

Among parameters:

• min_error_probability - minimum error probability (as in the paper)
• additional_confidence - confidence bias (as in the paper)
• special_tokens_fix to reproduce some reported results of pretrained models

For evaluation use M^2Scorer and ERRANT.

Text Simplification

The code and README for Text Simplification version of GECToR will be added soon.

Citation

If you find this work is useful for your research, please cite our paper:

@inproceedings{omelianchuk-etal-2020-gector,
    title = "{GECT}o{R} {--} Grammatical Error Correction: Tag, Not Rewrite",
    author = "Omelianchuk, Kostiantyn  and
      Atrasevych, Vitaliy  and
      Chernodub, Artem  and
      Skurzhanskyi, Oleksandr",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA → Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bea-1.16",
    pages = "163--170",
    abstract = "In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an F{\_}0.5 of 65.3/66.5 on CONLL-2014 (test) and F{\_}0.5 of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system.",
}