This code is for the paper "Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding" accepted at NAACL 2019.
Requirement: python>=2.7 tensorflow>=1.4.1 scikit-learn
We refer to /~https://github.com/MiuLab/Time-Decay-SLU (MiuLab repo).
- Put the dstc4 data on "./dstc4/" (Check the path in
parse_history.py) and runparse_history.pyto preprocess the data. (Then the preprocessed one will be located at "./Data".) - Download GloVe, modify line 33 in
slu_preprocess.pyto indicate the location of the glove file and run the code.
python slu.py \
--target [ALL, Guide, Tourist]
--level [sentence, role]
--talker_applied_to [Dist, Intent]
--att_to [Dist, Intent]
--att_out [Dist, Intent]
If you found this code useful, please cite the paper:
@inproceedings{kim-lee-2019-decay,
title = "Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding",
author = "Kim, Jonggu and Lee, Jong-Hyeok",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1372",
pages = "3718--3726",
}
Our implementation utilizes the code from MiuLab repo (/~https://github.com/MiuLab/Time-Decay-SLU).