Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer
This is the repository for the above mentioned paper. It can be found here: link
We provide the generated knowledge graphs (depth 0 & 1) for the
in data/knowledge_graphs/. Please download the datasets in data/dataset/. It will not work with the version from the official repositories. Please unzip all files before running scripts.
If you are interested in the graphs with depth > 1, please contact us. We will send you corresponding download links.
If you use the OpenDialKG dataset make sure to cite the authors correctly as well (citation information can be found in the linked dataset repository above).
To run the graph pre-processing described in Chapter 4.2 please use the following command:
python process_graphs.py --dataset komodis --depth 0 --encoding series
To run a training please use:
python train.py --dataset komodis --depth 0 --encoding series
@inproceedings{galetzka-etal-2021-space,
title = "Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer",
author = "Galetzka, Fabian and
Rose, Jewgeni and
Schlangen, David and
Lehmann, Jens",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.546",
doi = "10.18653/v1/2021.acl-long.546",
pages = "7028--7041"
}
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Copyright (c) 2021 Fabian Galetzka
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