SpaceFusion

SpaceFusion is a regularized multi-task learning paradigm proposed to align and structure the unstructured latent spaces learned by different models trained over different datasets. Of particular interest is its application to neural conversation modelling, where SpaceFusion is used to jointly optimize the relevance and diversity of generated responses.

News

The slides presented at NAACL-HLT 2019 is available.
We published a blog to discuss the intuition and implication
A Keras implementation is provided

Requirement

the code is tested using Python 3.6 and Keras 2.2.4

Dataset

Reddit data is downloaded from a third party. However, as we don't own the data, we cannot release it here. But this script can be used to process the raw data.
Switchboard data is downloaded from this repo
We provided a toy dataset in this repo for debugging.

Tokenization functions and other preprocessing tools can be found here.

Usage

To train a SpaceFusion model: python src/main.py mtask train --data_name=toy
To visualize the learned latent space: python src/vis.py --data_name=toy
To interact with the trained model: python src/main.py interact_rand --data_name=toy

Discription

main.py is the main file
model.py defines the SpaceFusion model (see class MTask) and some baselines
vis.py defines the function we used to visulize and analysis the latent space
dataset.py defines the data feeder
shared.py defines the default hyperparameters

Citation

Please cite our NAACL paper if this repo inspired your work :)

@article{gao2019spacefusion,
  title={Jointly Optimizing Diversity and Relevance in Neural Response Generation},
  author={Gao, Xiang and Lee, Sungjin and Zhang, Yizhe and Brockett, Chris and Galley, Michel and Gao, Jianfeng and Dolan, Bill},
  journal={NAACL-HLT 2019},
  year={2019}
}

shatu / SpaceFusion