# punctuation-restoration

**Repository Path**: segmentationFaults/punctuation-restoration

## Basic Information

- **Project Name**: punctuation-restoration
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-14
- **Last Updated**: 2022-01-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Punctuation Restoration using Transformer Models

This repository contins official implementation of the paper [*Punctuation Restoration using Transformer Models for High-and Low-Resource Languages*](https://aclanthology.org/2020.wnut-1.18/) accepted at the EMNLP workshop [W-NUT 2020](http://noisy-text.github.io/2020/).


## Data

#### English
English datasets are provided in `data/en` directory. These are collected from [here](https://drive.google.com/file/d/0B13Cc1a7ebTuMElFWGlYcUlVZ0k/view).

#### Bangla
Bangla datasets are provided in `data/bn` directory.


## Model Architecture
We fine-tune a Transformer architecture based language model (e.g., BERT) for the punctuation restoration task.
Transformer encoder is followed by a bidirectional LSTM and linear layer that predicts target punctuation token at
each sequence position.
![](./assets/model_architectue.png)


## Dependencies
Install PyTorch following instructions from [PyTorch website](https://pytorch.org/get-started/locally/). Remaining
dependencies can be installed with the following command
```bash
pip install -r requirements.txt
```


## Training
To train punctuation restoration model with optimal parameter settings for English run the following command
```
python src/train.py --cuda=True --pretrained-model=roberta-large --freeze-bert=False --lstm-dim=-1 
--language=english --seed=1 --lr=5e-6 --epoch=10 --use-crf=False --augment-type=all  --augment-rate=0.15 
--alpha-sub=0.4 --alpha-del=0.4 --data-path=data --save-path=out
```
To train for Bangla the corresponding command is
```
python src/train.py --cuda=True --pretrained-model=xlm-roberta-large --freeze-bert=False --lstm-dim=-1 
--language=bangla --seed=1 --lr=5e-6 --epoch=10 --use-crf=False --augment-type=all  --augment-rate=0.15 
--alpha-sub=0.4 --alpha-del=0.4 --data-path=data --save-path=out
```

#### Supported models for English
```
bert-base-uncased
bert-large-uncased
bert-base-multilingual-cased
bert-base-multilingual-uncased
xlm-mlm-en-2048
xlm-mlm-100-1280
roberta-base
roberta-large
distilbert-base-uncased
distilbert-base-multilingual-cased
xlm-roberta-base
xlm-roberta-large
albert-base-v1
albert-base-v2
albert-large-v2
```

#### Supported models for Bangla
```
bert-base-multilingual-cased
bert-base-multilingual-uncased
xlm-mlm-100-1280
distilbert-base-multilingual-cased
xlm-roberta-base
xlm-roberta-large
```


## Pretrained Models
You can find pretrained mdoels for RoBERTa-large model with augmentation for English [here](https://drive.google.com/file/d/17BPcnHVhpQlsOTC8LEayIFFJ7WkL00cr/view?usp=sharing)  
XLM-RoBERTa-large model with augmentation for Bangla can be found [here](https://drive.google.com/file/d/1X2udyT1XYrmCNvWtFpT_6jrWsQejGCBW/view?usp=sharing)



## Inference
You can run inference on unprocessed text file to produce punctuated text using `inference` module. Note that if the 
text already contains punctuation they are removed before inference. 

Example script for English:
```bash
python inference.py --pretrained-model=roberta-large --weight-path=roberta-large-en.pt --language=en 
--in-file=data/test_en.txt --out-file=data/test_en_out.txt
```
This should create the text file with following output:
```text
Tolkien drew on a wide array of influences including language, Christianity, mythology, including the Norse Völsunga saga, archaeology, especially at the Temple of Nodens, ancient and modern literature and personal experience. He was inspired primarily by his profession, philology. his work centred on the study of Old English literature, especially Beowulf, and he acknowledged its importance to his writings. 
```

Similarly, For Bangla
```bash
python inference.py --pretrained-model=xlm-roberta-large --weight-path=xlm-roberta-large-bn.pt --language=bn  
--in-file=data/test_bn.txt --out-file=data/test_bn_out.txt
```
The expected output is
```text
বিংশ শতাব্দীর বাংলা মননে কাজী নজরুল ইসলামের মর্যাদা ও গুরুত্ব অপরিসীম। একাধারে কবি, সাহিত্যিক, সংগীতজ্ঞ, সাংবাদিক, সম্পাদক, রাজনীতিবিদ এবং সৈনিক হিসেবে অন্যায় ও অবিচারের বিরুদ্ধে নজরুল সর্বদাই ছিলেন সোচ্চার। তার কবিতা ও গানে এই মনোভাবই প্রতিফলিত হয়েছে। অগ্নিবীণা হাতে তার প্রবেশ, ধূমকেতুর মতো তার প্রকাশ। যেমন লেখাতে বিদ্রোহী, তেমনই জীবনে কাজেই "বিদ্রোহী কবি"। তার জন্ম ও মৃত্যুবার্ষিকী বিশেষ মর্যাদার সঙ্গে উভয় বাংলাতে প্রতি বৎসর উদযাপিত হয়ে থাকে। 
```

Please note that *Comma* includes commas, colons and dashes, *Period* includes full stops, exclamation marks 
and semicolons and *Question* is just question marks. 


## Test
Trained models can be tested on processed data using `test` module to prepare result.

For example, to test the best preforming English model run following command
```bash
python src/test.py --pretrained-model=roberta-large --lstm-dim=-1 --use-crf=False --data-path=data/test
--weight-path=weights/roberta-large-en.pt --sequence-length=256 --save-path=out
```
Please provide corresponding arguments for `pretrained-model`, `lstm-dim`, `use-crf` that were used during training the
model. This will run test for all data available in `data-path` directory.


## Cite this work

```
@inproceedings{alam-etal-2020-punctuation,
    title = "Punctuation Restoration using Transformer Models for High-and Low-Resource Languages",
    author = "Alam, Tanvirul  and
      Khan, Akib  and
      Alam, Firoj",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.wnut-1.18",
    pages = "132--142",
}
```