code/data for the NAACL'19 paper Jointly Optimizing Diversity and Relevance in Neural Response Generation

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.

More documents:

• our paper at NAACL'19 (long, oral).
• The slides presented at NAACL'19.
• We published a MSR blog to discuss the intuition and implication
• our follow-up work, StyleFusion at EMNLP'19
• our latest Dialogue Evaluation/Ranking models, DialogRPT, at EMNLP'20

the code is tested using Python 3.6 and Keras 2.2.4

We provided scripts to generate Reddit and process Switchboard datasets as well as a toy dataset in this repo for debugging.

Please check here for more details.

• 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 mtask interact --data_name=toy --method=?, where method can be greedy, rand, sampling or beam. We used rand in the paper
• To generate hypotheses for testing with the trained model: python src/main.py mtask test --data_name=toy
• To evaluate the generated hypotheses python src/eval.py --path_hyp=? --path_ref=? --wt_len=?, which outputs the precision, recall, and F1 as defined in the paper. You may want to first run this command with -len_only to find a proper wt_len that minimize the difference between the average length (number of tokens) of hypothesis and reference.

• 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

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}
}