few-shot-text-classification

Few-shot binary text classification with Induction Networks and Word2Vec weights initialization

Reference

This is an PyTorch implementation of IJCNLP 2019 paper Induction Networks for Few-Shot Text Classification.

Few-shot Classification

• Few-shot classification is a task in which a classifier must be adapted to accommodate new classes not seen in training, given only a few examples of each of these new classes.
• There is a large labeled training set with a set of classes. However, after training, the ultimate goal is to produce classifiers on the test set (a disjoint set of new classes), for which only a small labeled support set will be available.
• If the support set contains K labeled examples for each of the C unique classes, the target few-shot problem is called C-way K-shot problem.
• Usually, the K is too small to train a supervised classification model. Therefore meta-learning on the training set is necessary, in order to extract transferable knowledge that will help classify the test set more successfully.

Prerequisites

• Install the required packages by:

pip install -r requirements.txt

Parameters

• All the parameters are defined in config.ini.
• configparser is used to parse the parameters.

Dataset: Amazon Review Sentiment Classification (ARSC)

This dataset comes from NAACL 2018 paper Diverse Few-Shot Text Classification with Multiple Metrics.

• The dataset comprises English reviews for 23 types of products on Amazon.
• For each product domain, there are three different binary classification tasks. These buckets then form 23 x 3 = 69 tasks in total.
• 4 x 3 = 12 tasks from 4 test domains (Books, DVD, Electronics and Kitchen) are selected as test set, and there are only 5 examples as support set for each labels in the test set. There other 19 domains are train domains.

• Therefore, 2-way 5-shot learning model is needed to classify this dataset.

Download

• Download Gorov/DiverseFewShot_Amazon
• The text files are in Amazon_few_shot folder.

Process

python data.py

• Data for train, dev and test:
  • Train data: *.train files in train domains.
  • Dev data: *.trian files in test domains as support data and *.dev files in test domains as query data.
  • Test data: *.trian files in test domains as support data and *.test files in test domains as query data.
• Pre-process all the texts data.
• Extract vocabulary with train data. Vocabulary has size of 35913, with 0 as <pad> and 1 as <unk>.
• Index all the texts with the vocabulary.
• Training batch composition: 5 negative support data + 5 positive support data + 27 negative query data + 27 positive query data. In each batch, support data and query data are randomly divided. As this is a 2-way 5-shot problem, the 2-ways means the amount of labels (negative/positive) and the 5-shot means the size of support data with the same label.
• Dev batch composition: 5 negative support data + 5 positive support data + 54 query data. There are 183 batches in total.
• Test batch composition: 5 negative support data + 5 positive support data + 54 query data. There are 181 batches in total.
• Above all, the batch size is always 64.

Word2Vec

python word2vec.py

• It is hard to train without Word2Vec weights initialization! Therefore Word2Vec weights are necessary!
• Train Word2Vec model with texts in train data, 95584 texts in total.
• Get the weights of the Word2Vec model.
• Load the weights in the encoder module.

Model

• Encoder Module: bi-direction recurrent neural network with self-attention.
• Induction Module: dynamic routing induction algorithm.
• Relation Module: measure the correlation between each pair of query and class and output the relation scores.
• See the paper in the reference section (at the end) for details.

Train, Dev and Test

export CUDA_VISIBLE_DEVICES=1
python main.py

• Training Strategy: episode-based meta training
• Dev while training and record dev accuracy.
• Pick the checkpoint with the highest dev accuracy as the best model and test on it.

Result on ARSC

• tensorboard

tensorboard --logdir=log/

• result

Train Loss	Train Accuracy

Dev Accuracy (achieves the highest 0.8410 at episode 9200)	Test Accuracy (at episode 9200)	Test Accuracy (paper)
	0.8452	0.8563

Author

Zhongyu Chen