code/data for EMNLP'19 paper Structuring Latent Spaces for Stylized Response Generation.
Designed to build a stylized dialogue response generator, StyleFusion jointly learns from a conversational dataset and other formats of text (e.g., non-parallel, non-conversational stylized text dataset). In our EMNLP 2019 paper, we demonstrated its use to generate response in style of Sherlock Holmes and arXiv. StyleFusion is a generalized version of our previous work SpaceFusion.
More documents:
- our EMNLP'19 paper and poster.
- A nice introduction of our work (not official, by Shannon.AI, in Chinese)
In our paper, we trained the model using the following three datasets.
- Reddit: the conversational dataset, can be generated using the script hosted in our previous SpaceFusion work.
- Sherlock Holmes-style dataset is avaialble here
- arXiv-style dataset can be obtained following instructions here
- to train a model
python src/main.py train - to interact with a trained model
python src/main.py cmd --restore=[path_to_model_file] - using the provided style classifiers
- interactive demo:
python src/classifier.py [fld_clf], where[fld_clf]is the folder where the classifier model exists, e.g.,classifier/Reddit_vs_arXiv/neural - evaluate a tsv file:
python src/classifier.py [fld_clf] [path_to_be_evaluated].
- interactive demo:
Please cite our EMNLP paper if this repo is useful to your work :)
@article{gao2019stylefusion,
title={Structuring Latent Spaces for Stylized Response Generation},
author={Gao, Xiang and Zhang, Yizhe and Lee, Sungjin and Galley, Michel and Brockett, Chris and Gao, Jianfeng and Dolan, Bill},
journal={EMNLP 2019},
year={2019}
}
