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Few-shot Text Classification with Distributional Signatures

This repository contains the code and data for our ICLR 2020 paper:

Few-shot Text Classification with Distributional Signatures. Yujia Bao, Menghua Wu, Shiyu Chang and Regina Barzilay.

If you find this work useful and use it on your own research, please cite our paper.

@inproceedings{
	bao2020fewshot,
	title={Few-shot Text Classification with Distributional Signatures},
	author={Yujia Bao and Menghua Wu and Shiyu Chang and Regina Barzilay},
	booktitle={International Conference on Learning Representations},
	year={2020}
}

Overview

Our goal is to improve few-shot classification performance by learning high-quality attention from the distributional signatures of the inputs. Given a particular episode, we extract relevant statistics from the source pool and the support set. Since these statistics only roughly approximate word importance for classification, we utilize an attention generator to translate them into high-quality attention that operates over words. This generated attention provides guidance for the downstream predictor, a ridge regressor, to quickly learn from a few labeled examples.

For further details on the model and various baselines, please see src/README.md.

Data

Download

We ran experiments on a total of 6 datasets. You may download our processed data here.

Format

• Each JSON file contains one example per line. With the exception of RCV1, each example has keys text and label. text is a list of input tokens and label is an integer, ranging from 0 to the number of classes - 1.
• For RCV1, we are unable to distribute the original data. In place of input tokens, each example specifies a path, which corresponds to each example's file path in the data distribution.
• Class splits for each dataset may be found in src/dataset/loader.py.

Quickstart

Run our model with default settings. By default we load data from data/.

./bin/our.sh

Scripts for other baselines may be found under bin/.

Code

src/main.py may be run with one of three modes: train, test, and finetune.

• train trains the meta-model using episodes sampled from the training data.
• test evaluates the current meta-model on 1000 episodes sampled from the testing data.
• finetune trains a fully-supervised classifier on the training data and finetunes it on the support set of each episode, sampled from the testing data.

Dependencies

• Python 3.7
• PyTorch 1.1.0
• numpy 1.15.4
• torchtext 0.4.0
• pytorch-transformers 1.1.0
• termcolor 1.1.0
• tqdm 4.32.2

Mac OS

As tryambak007 pointed out, there is an error when using multiprocessing.Queue. Please check out his fork (https://github.com/tryambak007/Distributional-Signatures) for the fix.