NeuSpell: A Neural Spelling Correction Toolkit

Contents

• Installation & Quick Start
• Toolkit
  • Introduction
  • Pretrained models
  • Datasets
  • Demo Setup
• Finetuning on custom data and creating new models
• Applications
• Additional Requirements

Updates

• April 2021:
  • neuspell is now available through pip. See Installation through pip
  • Added support for different transformer-based models such DistilBERT, XLM-RoBERTa, etc. See Finetuning on custom data and creating new models section for more details.
• March, 2021: Code-base reformatted. Addressed some bug fixes.
• November, 2020: Neuspell's BERT pretrained model is now available as part of huggingface models as murali1996/bert-base-cased-spell-correction. We provide an example code snippet at ./scripts/huggingface for curious practitioners.
• September, 2020: This work is accepted at EMNLP 2020 (system demonstrations)

Installation

git clone https://github.com/neuspell/neuspell; cd neuspell
pip install -e .

To install extra requirements,

pip install -r extras-requirements.txt

or individually as:

pip install -e .[elmo]
pip install -e .[spacy]

NOTE: For zsh, use ".[elmo]" and ".[spacy]" instead

Additionally, spacy models can be downloaded as:

python -m spacy download en_core_web_sm

Follow Additional Requirements for installing non-neural spell checkers- Aspell and Jamspell.

Then, download pretrained models of neuspell following Pretrained models

Here is a quick-start code snippet (command line usage). (See test.py for more usage patterns)

import neuspell

""" see available checkers """
print(f"available checkers: {neuspell.__all__}")
# → available checkers: ['BertsclstmChecker', 'CnnlstmChecker', 'NestedlstmChecker', 'SclstmChecker', 'SclstmbertChecker', 'BertChecker', 'SclstmelmoChecker', 'ElmosclstmChecker']

""" select spell checkers & load """
checker = BertChecker()
checker.from_pretrained()

""" spell correction """
checker.correct("I luk foward to receving your reply")
# → "I look forward to receiving your reply"
checker.correct_strings(["I luk foward to receving your reply", ])
# → ["I look forward to receiving your reply"]
checker.correct_from_file(src="noisy_texts.txt")
# → "Found 450 mistakes in 322 lines, total_lines=350"

""" evaluation of models """
checker.evaluate(clean_file="bea60k.txt", corrupt_file="bea60k.noise.txt")
# → data size: 63044
# → total inference time for this data is: 998.13 secs
# → total token count: 1032061
# → confusion table: corr2corr:940937, corr2incorr:21060,
#                    incorr2corr:55889, incorr2incorr:14175
# → accuracy is 96.58%
# → word correction rate is 79.76%

Alternatively, once can also select and load a spell checker differently as follows:

from neuspell import SclstmChecker

checker = SclstmChecker()
checker = checker.add_("elmo", at="input")  # "elmo" or "bert", "input" or "output"
checker.from_pretrained()

This feature of adding ELMO or BERT model is currently supported for selected models. See List of neural models in the toolkit for details.

Installation through pip

pip install neuspell

In v1.0, allennlp library is not installed which is used for models containing ELMO. To utilize them, do a source install as in Installation & Quick Start

Introduction

NeuSpell is an open-source toolkit for context sensitive spelling correction in English. This toolkit comprises of 10 spell checkers, with evaluations on naturally occurring mis-spellings from multiple (publicly available) sources. To make neural models for spell checking context dependent, (i) we train neural models using spelling errors in context, synthetically constructed by reverse engineering isolated mis-spellings; and (ii) use richer representations of the context.This toolkit enables NLP practitioners to use our proposed and existing spelling correction systems, both via a simple unified command line, as well as a web interface. Among many potential applications, we demonstrate the utility of our spell-checkers in combating adversarial misspellings.

Live demo available at http://neuspell.github.io/

List of neural models in the toolkit:

• CNN-LSTM
• SC-LSTM
• Nested-LSTM
• BERT
• SC-LSTM plus ELMO (at input)
• SC-LSTM plus ELMO (at output)
• SC-LSTM plus BERT (at input)
• SC-LSTM plus BERT (at output)

This pipeline corresponds to the `SC-LSTM plus ELMO (at input)` model.

Performances

Spell Checker	Word Correction Rate	Time per sentence (in milliseconds)
Aspell	48.7	7.3*
Jamspell	68.9	2.6*
CNN-LSTM	75.8	4.2
SC-LSTM	76.7	2.8
Nested-LSTM	77.3	6.4
BERT	79.1	7.1
SC-LSTM plus ELMO (at input)	79.8	15.8
SC-LSTM plus ELMO (at output)	78.5	16.3
SC-LSTM plus BERT (at input)	77.0	6.7
SC-LSTM plus BERT (at output)	76.0	7.2

Performance of different correctors in the NeuSpell toolkit on the BEA-60K dataset with real-world spelling mistakes. ∗ indicates evaluation on a CPU (for others we use a GeForce RTX 2080 Ti GPU).

Pretrained models

Download Checkpoints

Run the following to download checkpoints of all neural models

cd data/checkpoints
python download_checkpoints.py 

See data/checkpoints/README.md for more details. You can alternatively choose to download only selected models' checkpoints.

Datasets

Download datasets

Run the following to download datasets

cd data/traintest
python download_datafiles.py 

See data/traintest/README.md for more details.

Synthetic Training Dataset Creation

The toolkit offers 4 kinds of noising strategies to generate synthetic parallel training data to train neural models for spell correction.

• RANDOM
• Word Replacement
• Probabilistic Replacement
• A combination of Word Replacement and Probabilistic Replacement

Train files are dubbed with names .random, .word, .prob, .probword respectively. For each strategy, we noise ∼20% of the tokens in the clean corpus. We use 1.6 Million sentences from the One billion word benchmark dataset as our clean corpus.

Demo Setup

In order to setup a demo, follow these steps:

• Do Installation
• Download checkpoints
• Start a flask server at ./scripts/flask-server by running CUDA_VISIBLE_DEVICES=0 python app.py (on GPU) or python app.py (on CPU)

Finetuning on custom data and creating new models

Finetuning on top of neuspell pretrained models

from neuspell import BertChecker

checker = BertChecker()
checker.from_pretrained()
checker.finetune(clean_file="sample_clean.txt", corrupt_file="sample_corrupt.txt", data_dir="default")

This feature is only available for BertChecker and ElmosclstmChecker.

Training other Transformers/BERT-based models

We now support initializing a huggingface model and finetuning it on your custom data. Here is a code snippet demonstrating that:

First mark your files containing clean and corrupt texts in a line-seperated format

from neuspell.commons import DEFAULT_TRAINTEST_DATA_PATH

data_dir = DEFAULT_TRAINTEST_DATA_PATH
clean_file = "sample_clean.txt"
corrupt_file = "sample_corrupt.txt"

from neuspell.seq_modeling.helpers import load_data, train_validation_split
from neuspell.seq_modeling.helpers import get_tokens
from neuspell import BertChecker

# Step-0: Load your train and test files, create a validation split
train_data = load_data(data_dir, clean_file, corrupt_file)
train_data, valid_data = train_validation_split(train_data, 0.8, seed=11690)

# Step-1: Create vocab file. This serves as the target vocab file and we use the defined model's default huggingface
# tokenizer to tokenize inputs appropriately.
vocab = get_tokens([i[0] for i in train_data], keep_simple=True, min_max_freq=(1, float("inf")), topk=100000)

# # Step-2: Initialize a model
checker = BertChecker(device="cuda")
checker.from_huggingface(bert_pretrained_name_or_path="distilbert-base-cased", vocab=vocab)

# Step-3: Finetune the model on your dataset
checker.finetune(clean_file=clean_file, corrupt_file=corrupt_file, data_dir=data_dir)

You can further evaluate your model on a custom data as follows:

from neuspell import BertChecker

checker = BertChecker()
checker.from_pretrained(
    bert_pretrained_name_or_path="distilbert-base-cased",
    ckpt_path=f"{data_dir}/new_models/distilbert-base-cased"  # "<folder where the model is saved>"
)
checker.evaluate(clean_file=clean_file, corrupt_file=corrupt_file, data_dir=data_dir)

Multilingual Models

Following usage above, once can now seamlessly utilize multilingual models such as xlm-roberta-base, bert-base-multilingual-cased and distilbert-base-multilingual-cased on a non-English script.

Potential applications for practitioners

• Defenses against adversarial attacks in NLP
  • example implementation available in folder ./applications/Adversarial-Misspellings-arxiv. See README.md.
• Improving OCR text correction systems
• Improving grammatical error correction systems
• Improving Intent/Domain classifiers in conversational AI
• Spell Checking in Collaboration and Productivity tools

Additional requirements

Requirements for Aspell checker:

wget https://files.pythonhosted.org/packages/53/30/d995126fe8c4800f7a9b31aa0e7e5b2896f5f84db4b7513df746b2a286da/aspell-python-py3-1.15.tar.bz2
tar -C . -xvf aspell-python-py3-1.15.tar.bz2
cd aspell-python-py3-1.15
python setup.py install

Requirements for Jamspell checker:

sudo apt-get install -y swig3.0
wget -P ./ https://github.com/bakwc/JamSpell-models/raw/master/en.tar.gz
tar xf ./en.tar.gz --directory ./

Citation

@inproceedings{jayanthi-etal-2020-neuspell,
    title = "{N}eu{S}pell: A Neural Spelling Correction Toolkit",
    author = "Jayanthi, Sai Muralidhar  and
      Pruthi, Danish  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = oct,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.21",
    doi = "10.18653/v1/2020.emnlp-demos.21",
    pages = "158--164",
    abstract = "We introduce NeuSpell, an open-source toolkit for spelling correction in English. Our toolkit comprises ten different models, and benchmarks them on naturally occurring misspellings from multiple sources. We find that many systems do not adequately leverage the context around the misspelt token. To remedy this, (i) we train neural models using spelling errors in context, synthetically constructed by reverse engineering isolated misspellings; and (ii) use richer representations of the context. By training on our synthetic examples, correction rates improve by 9{\%} (absolute) compared to the case when models are trained on randomly sampled character perturbations. Using richer contextual representations boosts the correction rate by another 3{\%}. Our toolkit enables practitioners to use our proposed and existing spelling correction systems, both via a simple unified command line, as well as a web interface. Among many potential applications, we demonstrate the utility of our spell-checkers in combating adversarial misspellings. The toolkit can be accessed at neuspell.github.io.",
}

You can contact the authors at jsaimurali001 [at] gmail [dot] com