Awesome

MixText

This repo contains codes for the following paper:

Jiaao Chen, Zichao Yang, Diyi Yang: MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification. In Proceedings of the 58th Annual Meeting of the Association of Computational Linguistics (ACL'2020)

If you would like to refer to it, please cite the paper mentioned above.

Getting Started

These instructions will get you running the codes of MixText.

Requirements

• Python 3.6 or higher
• Pytorch >= 1.3.0
• Pytorch_transformers (also known as transformers)
• Pandas, Numpy, Pickle
• Fairseq

Code Structure

|__ data/
        |__ yahoo_answers_csv/ --> Datasets for Yahoo Answers
            |__ back_translate.ipynb --> Jupyter Notebook for back translating the dataset
            |__ classes.txt --> Classes for Yahoo Answers dataset
            |__ train.csv --> Original training dataset
            |__ test.csv --> Original testing dataset
            |__ de_1.pkl --> Back translated training dataset with German as middle language
            |__ ru_1.pkl --> Back translated training dataset with Russian as middle language

|__code/
        |__ transformers/ --> Codes copied from huggingface/transformers
        |__ read_data.py --> Codes for reading the dataset; forming labeled training set, unlabeled training set, development set and testing set; building dataloaders
        |__ normal_bert.py --> Codes for BERT baseline model
        |__ normal_train.py --> Codes for training BERT baseline model
        |__ mixtext.py --> Codes for our proposed TMix/MixText model
        |__ train.py --> Codes for training/testing TMix/MixText 

Downloading the data

Please download the dataset and put them in the data folder. You can find Yahoo Answers, AG News, DB Pedia here, IMDB here.

Pre-processing the data

For Yahoo Answer, We concatenate the question title, question content and best answer together to form the text to be classified. The pre-processed Yahoo Answer dataset can be downloaded here.

Note that for AG News and DB Pedia, we only utilize the content (without titles) to do the classifications, and for IMDB we do not perform any pre-processing.

We utilize Fairseq to perform back translation on the training dataset. Please refer to ./data/yahoo_answers_csv/back_translate.ipynb for details.

Here, we have put two examples of back translated data, de_1.pkl and ru_1.pkl, in ./data/yahoo_answers_csv/ as well. You can directly use them for Yahoo Answers or generate your own back translated data followed the ./data/yahoo_answers_csv/back_translate.ipynb.

Training models

These section contains instructions for training models on Yahoo Answers using 10 labeled data per class for training.

Training BERT baseline model

Please run ./code/normal_train.py to train the BERT baseline model (only use labeled training data):

python ./code/normal_train.py --gpu 0,1 --n-labeled 10 --data-path ./data/yahoo_answers_csv/ \
--batch-size 8 --epochs 20 

Training TMix model

Please run ./code/train.py to train the TMix model (only use labeled training data):

python ./code/train.py --gpu 0,1 --n-labeled 10 --data-path ./data/yahoo_answers_csv/ \
--batch-size 8 --batch-size-u 1 --epochs 50 --val-iteration 20 \
--lambda-u 0 --T 0.5 --alpha 16 --mix-layers-set 7 9 12 --separate-mix True 

Training MixText model

Please run ./code/train.py to train the MixText model (use both labeled and unlabeled training data):

python ./code/train.py --gpu 0,1,2,3 --n-labeled 10 \
--data-path ./data/yahoo_answers_csv/ --batch-size 4 --batch-size-u 8 --epochs 20 --val-iteration 1000 \
--lambda-u 1 --T 0.5 --alpha 16 --mix-layers-set 7 9 12 \
--lrmain 0.000005 --lrlast 0.0005