This is a PyTorch implementation of the DialogBERT model described in DialogBERT: Neural Response Generation via Hierarchical BERT with Distributed Utterance Order Ranking.
- Python 3.6
- PyTorch
Install packages of the requirements.txt file.
- Train model by
python main.py
The logs and temporary results will be printed to stdout and saved in the ./output path.
- Run test by
where XXXXX specifies the iteration number of the optimal checkpoint.
python main.py --do_test --reload_from XXXXX
BSD 3-clause
DialogBERT
Copyright 2021-present NAVER Corp.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
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* Neither the name of the copyright holder nor the names of its
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
If you use any source code included in this toolkit in your work, please cite the following paper:
@inproceedings{gu2021dialogbert,
title={Dialog{BERT}: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances},
author={Gu, Xiaodong and Yoo, Kang Min and Ha, Jung-Woo},
journal={In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021)},
year={2021}
}