mahfujur1 / diversity-based-generalization

Unsupervised Domain Adaptation in Text (ECML-PKDD '20)

Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift

Purpose of the model: Train a classifier in one domain where plenty of data is available and generalize it to another domain that has no (labeled or unlabeled) data.

Paper/Cite

https://arxiv.org/abs/2002.10937 (To appear at ECML-PKDD 2020)

@article{krishnanDiversity,
  title={Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift},
  author={Krishnan, Jitin and Purohit, Hemant and Rangwala, Huzefa},
  journal={In Proceedings of the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
  year={2020}
}

Why use this method? (See paper for detailed performance comparison)

• Unlike the existing state-of-the-art unsupervised methods, no unlabeled target data is needed to train the model. Our model is out-of-the-box adaptable to any domain.
• Computationally much cheaper (days to a few hours on a cpu) as it does not use unlabeled target data (which means no gradient reversal or manual pivot extractions), with no trade-off in performance.

Results on Blitzer Dataset

Task	Accuracy in %
books-dvd	87.46
books-electronics	86.08
books-kitchen	87.68
kitchen-books	84.23
kitchen-dvd	83.34
kitchen-electronics	89.22
electronics-books	84.33
electronics-kitchen	91.05
electronics-dvd	82.81
dvd-books	88.74
dvd-electronics	86.21
dvd-kitchen	87.37
Average	86.54

Results on the newly labeled Crisis Dataset

Task	Accuracy in %
Harvey-Florence	78.11
Harvey-Irma	64.38

Results on very divergent datasets such as Yelp and IMDb, in addition to Amazon Reviews. (3 randomly selected combinations).

Task	Accuracy in %
Electronics-Yelp	89.15
Kitchen-IMDb	78.33
Yelp-IMDb	77.28

Requirements

Python3.6, Keras, Tensorflow. Or pip install -r requirements.txt to install necessary packages.

Additional Requirements

Download GoogleNews-vectors-negative300.bin

Data

All datasets in raw_data folder. @user mentions anonymized for twitter data.

To add new dataset:

Place your positive/negative/unlabeled in the raw_data folder (no preprocessing needed) and name the files accordingly.

Sample Runs of all models

BiLSTM

python bilstm.py 'electronics' 'kitchen

BiLSTM + Attention

python bilstm_attention.py 'electronics' 'kitchen

BiLSTM + MHA

python bilstm_mha.py 'electronics' 'kitchen'

BiLSTM + MHAD

python bilstm_mhad.py 'electronics' 'kitchen'

BiLSTM + MHAD + Tri-I

python bilstm_mhad_tri_I.py 'electronics' 'kitchen'

BiLSTM + MHAD + Tri-II

python bilstm_mhad_tri_II.py 'electronics' 'kitchen'

Contact information

For help or issues, please submit a GitHub issue or contact Jitin Krishnan ([email protected]).