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InPHYNet

This repository contains the code for the paper titled "InPHYNet: Leveraging attention-based multitask recurrent networks for multi-label physics text classification". The code was tested on Python 3.6.8, PyTorch 1.3.1 and Keras 2.3.1 with Tensorflow 1.14.0 backend.

Getting Started

Clone this repository using the following command:

git clone https://github.com/abhishekag03/InPHYNet/
  • The TREC data can be found at this link. Download this data and put it within data folder.
  • The SST data is directly loaded from the official torchtext repository. For official documentation, refer this.
  • Install all the dependencies using the following command:
pip install -r requirements.txt
  • Download the pretrained Spacy English model using the following command:
python -m spacy download en_core_web_sm
  • Install the stopwords in the nltk installation using the following commands within your python interpretor:
>>> import nltk
>>> nltk.download('stopwords')

Baselines

We use the following models as our baseline models for the primary Physics text multi-label classification:

  • Gaussian Naive Bayes
  • Decision Tree
  • Random Forest
  • Multi Layer Perceptron
  • Multi-Label KNN

We also utilise the following label transformation techniques to convert the multi-label problem into a more feasible multi-class one:

  • Label Powerset
  • Classifier Chain
  • Binary Relevance

We use two types of input embeddings:

  • Doc2Vec
  • TF-IDF

To train and evaluate the baseline models on Doc2Vec embeddings, use the following command:

python baseline_doc2vec.py

To train and evaluate the baseline models on TF-IDF embeddings, use the following command:

python baseline_tfidf.py

The results of baseline models for both of Doc2Vec and TF-IDF embeddings are present in baseline_doc2vec_results.txt and baseline_tfidf_results.txt respectively.

Training

To train the vanilla LSTM on Doc2Vec embeddings, use the following command:

python train_vanilla_lstm_doc2vec.py

To train the vanilla LSTM on TF-IDF embeddings, use the following command:

python train_vanilla_lstm_tfidf.py

To train and evaluate InPHYNet with a single auxiliary task (TREC dataset) on Doc2Vec embeddings, use the following command:

python train_inPHYNet_doc2vec.py

The evaluation results will be printed after every epoch on the heldout test set.

To train and evaluate InPHYNet with a single auxiliary task (TREC dataset) on TF-IDF embeddings, use the following command:

python train_inPHYNet_tfidf.py

The evaluation results will be printed after every epoch on the heldout test set.

To train GIRNet on Doc2Vec embeddings, use the following command:

python train_GIRNet_doc2vec.py

To train GIRNet on TF-IDF embeddings, use the following command:

python train_GIRNet_tfidf.py

For extracting features from the BERT baseline, you can use the following script and use curr_tensor as the extracted feature for every sample.

from transformers import *

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

# x is list of input texts/paragraphs
for i, text in enumerate(x):
    input_ids = torch.tensor([tokenizer.encode(text)])
    with torch.no_grad():
        last_hidden_states = model(input_ids)[0]
        last_hidden_states = last_hidden_states.view(last_hidden_states.shape[1], last_hidden_states.shape[2])
        if(last_hidden_states.shape[0]>thresh): # thresh is threshold on the maximum sequence length allowed for each paragraph to ensure consistency across all samples.
          curr_tensor = last_hidden_states[:thresh]
        else:
          curr_tensor = torch.cat([last_hidden_states, torch.zeros((thresh-last_hidden_states.shape[0], last_hidden_states.shape[1]))], 0)

Once, the features are extracted, you can train an MLP network with BCE loss for the downstream primary task.

Evaluation

To test the vanilla LSTM performance on Doc2Vec embeddings, use the following command:

python evaluate_vanilla_lstm_doc2vec.py

To test the vanilla LSTM performance on TF-IDF embeddings, use the following command:

python evaluate_vanilla_lstm_tfidf.py

To test InPHYNet performance on a singe aux task (TREC dataset) on Doc2Vec embeddings, use the following command:

python train_inPHYNet_doc2vec.py

The evaluation results will be printed after every epoch on the heldout test set. To test InPHYNet performance on a singe aux task (TREC dataset) on TF-IDF embeddings, use the following command:

python train_inPHYNet_tfidf.py

The evaluation results will be printed after every epoch on the heldout test set. To test GIRNet performance on Doc2Vec embeddings, use the following command:

python evaluate_GIRNet_doc2vec.py

To test GIRNet performance on TFIDF embeddings, use the following command:

python evaluate_GIRNet_tfidf.py

Notes

  • To change the hyperparameters and other tunable parameters, update the flags.py with appropriate changes
  • Currently, the models are saved in the checkpoints_<x> folders and the result logs are updated in the results_<x> folders (where x indicates the trained model like baseline, vanilla_lstm, girnet, inphynet etc).
  • The num_aux parameter in flags.py is used to indicate the number of auxiliary tasks that are used to train InPHYNet. Change this parameter accordingly when training for single or multiple tasks.

Citation

If you find this work useful, please cite it as:

@article{udandarao2020inphynet,
  title={Inphynet: Leveraging attention-based multitask recurrent networks for multi-label physics text classification},
  author={Udandarao, Vishaal and Agarwal, Abhishek and Gupta, Anubha and Chakraborty, Tanmoy},
  journal={Knowledge-Based Systems},
  pages={106487},
  year={2020},
  publisher={Elsevier}
}

In case of questions, contact:

  • vishaal16119 [at] iiitd [dot] ac [dot] in
  • abhishek16126 [at] iiitd [dot] ac [dot] in

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A multi-task learning model which incorporates auxiliary semantics by utilising a weight alignment layer and information exchange layer

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