Improving Seq2Seq Grammatical Error Correction via Decoding Interventions

Houquan Zhou, Yumeng Liu, Zhenghua Li^✉️, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

^{Note: This cover image is created by DALL·E 3}

TL;DR

This repo contains the code for our EMNLP 2023 Findings paper: Improving Seq2Seq Grammatical Error Correction via Decoding Interventions.

We introduce a decoding intervention framework that uses critics to assess and guide token generation. We evaluate two types of critics: a pre-trained language model and an incremental target-side grammatical error detector. Experiments on English and Chinese data show our approach surpasses many existing methods and competes with SOTA models.

Citation

@inproceedings{zhou-etal-2023-improving-seq2seq,
    title = "Improving {S}eq2{S}eq Grammatical Error Correction via Decoding Interventions",
    author = "Zhou, Houquan  and
      Liu, Yumeng  and
      Li, Zhenghua  and
      Zhang, Min  and
      Zhang, Bo  and
      Li, Chen  and
      Zhang, Ji  and
      Huang, Fei",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.495",
    pages = "7393--7405",
}

Setup

Clone this repo recursively:

git clone https://github.com/Jacob-Zhou/gecdi.git --recursive

# The newest version of parser is not compatible with the current code, 
# so we need to checkout to a previous version
cd gecdi/3rdparty/parser/ && git checkout 6dc927b && cd -

Then you can use following commands to create an environment and install the dependencies:

. scripts/set_environment.sh

# for Errant (v2.0.0) evaluation a python 3.6 environment is required
# make sure your system has python 3.6 installed, then run:
. scripts/set_py36_environment.sh

You can follow this repo to obtain the 3-stage train/dev/test data for training a English GEC model. The multilingual datasets are available here.

Before running, you are required to preprocess each sentence pair into the format of

S   [src]
T   [tgt]

S   [src]
T   [tgt]

Where [src] and [tgt] are the source and target sentences, respectively. A \t is used to separate the prefix S or T and the sentence. Each sentence pair is separated by a blank line. See data/toy.train for examples.

Download Trained Models

The trained models are avaliable in HuggingFace model hub. You can download them by running:

# If you have not installed git-lfs, please install it first
# The installation guide can be found here: https://git-lfs.github.com/
# Most of the installation guide requires root permission.
# However, you can install it locally using conda:
# https://anaconda.org/anaconda/git-lfs

# Create directory for storing the trained models
mkdir -p models
cd models

# Download the trained models
# First, clone the small files
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/HQZhou/bart-large-gec
# Then use git-lfs to download the large files
cd bart-large-gec
git lfs pull

# Return to the models directory
cd -

# The download process is the same for the GED model
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/HQZhou/bart-large-ged
cd bart-large-ged
git lfs pull

# The download process is the same for the Chinese models
# Just change the GEC url to https://huggingface.co/HQZhou/bart-large-chinese-gec
# and the GED url to https://huggingface.co/HQZhou/bart-large-chinese-ged

The models can also download by using the huggingface-cli:

# First make sure that you have installed `huggingface_hub` package
# You can install it following the guide here: https://huggingface.co/docs/huggingface_hub/installation
huggingface-cli download HQZhou/bart-large-gec --local-dir-use-symlinks False --local-dir models/bart-large-gec
huggingface-cli download HQZhou/bart-large-ged --local-dir-use-symlinks False --local-dir models/bart-large-ged

Run

English experiments:

# Baseline (vanilla decoding)
bash pred.sh  \
    devices=0  \
    gec_path=models/bart-large-gec/model  \
    dataset=bea19.dev

# w/ LM-critic
bash pred.sh  \
    devices=0  \
    gec_path=models/bart-large-gec/model  \
    lm_alpha=0.8 lm_beta=10  \
    dataset=bea19.dev

# w/ GED-critic
bash pred.sh  \
    devices=0  \
    gec_path=models/bart-large-gec/model  \
    ged_path=models/bart-large-ged/model  \
    ged_alpha=0.8 ged_beta=1  \
    batch=500  \
    dataset=bea19.dev

# w/ both LM-critic and GED-critic
bash pred.sh  \
    devices=0  \
    gec_path=models/bart-large-gec/model  \
    ged_path=models/bart-large-ged/model  \
    lm_alpha=0.8 lm_beta=10  \
    ged_alpha=0.8 ged_beta=1  \
    batch=250  \
    dataset=bea19.dev

Chinese experiments:

# Baseline (vanilla decoding)
bash pred.sh  \
    devices=0  \
    dataset=mucgec.dev

# w/ LM-critic
bash pred.sh  \
    devices=0  \
    lm_alpha=0.3  \
    lm_beta=0.1  \
    dataset=mucgec.dev

# w/ GED-critic
bash pred.sh  \
    devices=0  \
    ged_alpha=0.6 ged_beta=10  \
    dataset=mucgec.dev

# w/ both LM-critic and GED-critic
bash pred.sh  \
    devices=0  \
    lm_alpha=0.3 lm_beta=0.1  \
    ged_alpha=0.6 ged_beta=10  \
    dataset=mucgec.dev

Run target-side GED only:

bash pred_ged.sh  \
    devices=0  \
    path=models/bart-large-ged/model  \
    data=<path to the parallel data to be detected>  \
    pred=<path to the output file>

# the input file should be in the format of:
# S   [src 0]
# T   [tgt 0]

# S   [src 1]
# T   [tgt 1]

# the output file will be in the format of jsonl as follows:
# {
#     "src_text": "I implicated my class from winning the champion .",
#     "tgt_text": "I implicated my class in winning the champion .",
#     "tgt_subword": ["ĠI", "Ġimplicated", "Ġmy", "Ġclass", "Ġin", "Ġwinning", "Ġthe", "Ġchampion", "Ġ."],
#     "error": [[1, 2, "SUB"], [4, 5, "SUB"]]
# }

# the error field is a list of error spans, each span is represented as a list of three elements:
# [start of subword span, end of subword span, error type]
# error type can be one of the following:
# `RED`: redundant
# `SUB`: substitution
# `MISS-L`: there are missing tokens on the left side of the span

Recommended Hyperparameters

We search the coefficient $\alpha$ and $\beta$ on the development set.

The optimal coefficients are varied across different datasets.

Hyperparameters for LM-critic:

Dataset	$\alpha$	$\beta$
CoNLL-14	0.8	10.0
BEA-19	0.8	10.0
GMEG-Wiki	1.0	10.0
MuCGEC	0.3	0.1

Hyperparameters for GED-critic:

Dataset	$\alpha$	$\beta$
CoNLL-14	0.8	1.0
BEA-19	0.8	1.0
GMEG-Wiki	0.9	1.0
MuCGEC	0.6	10.0

Typo

• Appendix B.2 (STAGE 3): We further fine-tune the model on the W&I + LOCNESS test set only. $\rightarrow$ We further fine-tune the model on the W&I + LOCNESS training set only. (We sincerely apologize for this typo and thank @GMago123 for pointing it out in the issue#4)