Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction

Installation

The following command installs all necessary packages:

pip install -r requirements.txt

python -m spacy download en_core_web_sm

The project was tested using Python 3.7.

Datasets

All the public GEC datasets can be downloaded from here.
Knowledge distilled datasets can be downloaded here.
Synthetically PIE 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

All available pretrained models can be downloaded here.

Pretrained encoder	Confidence bias	Min error prob	BEA-2019 (test)
RoBERTa [link]	0.1	0.65	73.1
Large RoBERTa voc10k + DeBERTa voc10k + XLNet voc 5k [link]	0.3	0.55	76.05

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, roberta, deberta, xlnet, bert-large, roberta-large, deberta-large, xlnet-large} model encoder
• tn_prob probability of getting sentences with no errors; helps to balance precision/recall

In our experiments we had 98/2 train/dev split on each training stage.

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

Ensembling by averaging of output tag probabilities

For evaluating ensemble you need to name your models like "xlnet_1_SOMETHING.th", "roberta_1_SOMETHING.th" and pass them all to model_path parameter. You also need to set is_ensemble parameter.

python predict.py --model_path MODEL_PATH MODEL_PATH [MODEL_PATH ...] \
                  --vocab_path VOCAB_PATH --input_file INPUT_FILE \
                  --output_file OUTPUT_FILE \
                  --is_ensemble 1

Ensembling by majority votes on output edit spans

For this ensemble, you first need to predict output files by singel models and them combine these files by script

python ensemble.py --source_file SOURCE_FILE \
                         --target_files TARGET_FILE TARGET_FILE [TARGET_FILE ...]
                         --output_file OUTPUT_FILE

Evaluation

For evaluation we use ERRANT.

Citation

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

Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction

@inproceedings{tarnavskyi-etal-2022-improved-gector,
    title = "Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction",
    author = "Tarnavskyi, Maksym and Chernodub, Artem and Omelianchuk, Kostiantyn",
    booktitle = "Accepted for publication at 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)",
    month = May,
    year = "2022",
    address = "Dublin, Ireland",
    url = "https://arxiv.org/pdf/2203.13064.pdf",
    abstract = "In this paper, we investigate improvements to the GEC sequence tagging architecture with a focus on ensembling of recent cutting-edge Transformer-based encoders in Large configurations. We encourage ensembling models by majority votes on span-level edits because this approach is tolerant to the model architecture and vocabulary size. Our best ensemble achieves a new SOTA result with an F0.5 score of 76.05 on BEA-2019 (test), even without pretraining on synthetic datasets. In addition, we perform knowledge distillation with a trained ensemble to generate new synthetic training datasets, "Troy-Blogs" and "Troy-1BW". Our best single sequence tagging model that is pretrained on the generated Troy- datasets in combination with the publicly available synthetic PIE dataset achieves a near-SOTA result with an F0.5 score of 73.21 on BEA-2019 (test). The code, datasets, and trained models are publicly available.",
}