README.md

Learn to Adapt

This repository is the re-implementation of "Learn to Adapt for Generalized Zero-Shot Text Classification" (ACL 2022 main conference). The reproducibility report can be found here under the name "report final lta".

Requirements

1. First git clone our repository.

   git clone https://github.com/Shrushti1999/Learn-To-Adapt.git

2. To setup the environment, we recommend to use a terminal that supports GPU.

   conda create -n lta python=3.7.9

   source ~/.bashrc

   Activate the conda environment lta.

   conda activate lta

3. After we setup basic conda environment, install torch and gensim.

   pip install torch

   pip install gensim==3.8.1

   Later install requirements.

   pip install -r requirements.txt

Datasets and Files

1. Download word2vec English resource https://github.com/eyaler/word2vec-slim/blob/master/GoogleNews-vectors-negative300-SLIM.bin.gz, and put the unzip bin file to the directory data/resources.

2. Preprocess the dataset

   python data_preprocess.py

   And pickle files will be stored in data/ver1/CLINC.

Training and Evaluation

We use configuration files to store hyper-parameters for experiments in config_clinc_LTSM.json

To train the Metric Learning model, run this command:

python train.py -d 0 -st1 1 -c config_Clinc_LSTM.json

where -st1 means step 1. If you want to run LTA w/o init, you do not need to run step 1 first.

To train the LTA in the paper, run step 2:

python train.py -d 0 -st2 1 -c config_Clinc_LSTM.json

Or you can change any configurations in the json file, to test different hyper-parameters.

Robustness

Cut the train_seen.csv and paste it in the Learn-To-Adapt folder. Run the Robustness.ipynb file to generate the noisy dataset. You can also see the dataset train_seen Robustness.cs

Multilinguality

Cut the train_seen.csv and paste it in the Learn-To-Adapt folder. Run the Multilinguality.ipynb file to generate the multilingual dataset. You can also see the dataset train_seen Multilinguality.cs

Reference

@inproceedings{zhang-etal-2022-learn,
    title = "Learn to Adapt for Generalized Zero-Shot Text Classification",
    author = "Zhang, Yiwen  and
      Yuan, Caixia  and
      Wang, Xiaojie  and
      Bai, Ziwei  and
      Liu, Yongbin",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.39",
    doi = "10.18653/v1/2022.acl-long.39",
    pages = "517--527",
    abstract = "Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the parameters keep stationary in predicting procedures. To address these challenges, we propose a novel Learn to Adapt (LTA) network using a variant meta-learning framework. Specifically, LTA trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning (GZSL) scenario in accordance with the test time, and simultaneously learns to calibrate the class prototypes and sample representations to make the learned parameters adaptive to incoming unseen classes. We claim that the proposed model is capable of representing all prototypes and samples from both classes to a more consistent distribution in a global space. Extensive experiments on five text classification datasets show that our model outperforms several competitive previous approaches by large margins. The code and the whole datasets are available at https://github.com/Quareia/LTA.",
}