This is the code of this paper: [Task-oriented Dialogue System for Automatic Diagnosis](http://www.aclweb.org/anthology/P18-2033).
A Medical-domain Dialogue System for Diseases Identification.
Both the symptoms and the diseases are treated as slots as in the conventional dialogue systems.
There is only one agent in the dialogue system, who is in charge of both symptom selection and disease prediction.
# Data
Our dataset is available [here](https://github.com/LiuQL2/MedicalChatbot/blob/develop/acl2018-mds.zip) or can be found at the homepage of [Prof. Wei](http://www.sdspeople.fudan.edu.cn/zywei/). Please read the README.txt in the zip file and our paper for the details about our dataset.
# How to use
1. Downloading our [dataset](http://www.sdspeople.fudan.edu.cn/zywei/data/acl2018-mds.zip).
2. Putting the dataset in a directory and pointing the path of the dataset in src/dialogue_system/run/run.py
3. Using the following command to see how to run this code, i.e., each parameter in the code.
```
cd src/dialogue_system/run
python run.py --help
```
# Cite
```
@inproceedings{wei2018task,
title={Task-oriented Dialogue System for Automatic Diagnosis},
author={Liu, Qianlong and Wei, Zhongyu and Peng, Baolin and Tou, Huaixiao and Chen, Ting and Huang, Xuanjing and Wong, Kam-Fai and Dai, Xiangying},
booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
volume={2},
pages={201--207},
year={2018}
}
```
面向任务的自动诊断对话系统.zip
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面向任务的自动诊断对话系统.zip （95个子文件） 
面向任务的自动诊断对话系统 
ploter.py 2KB others __init__.py 0B util.py 4KB ptb_word_lm.py 20KB reader.py 4KB __pycache__ reader.cpython-36.pyc 4KB util.cpython-36.pyc 3KB __init__.cpython-36.pyc 145B result checkpoint 81B word_embedding.py 1KB graph.pbtxt 18.38MB events.out.tfevents.1512977904.LIUQIANLONG 10.96MB events.out.tfevents.1512975107.LIUQIANLONG 10.96MB model.ckpt-0.meta 5.49MB model.ckpt-0.data-00000-of-00001 17.74MB model.ckpt-0.index 470B preprocess __init__.py 0B match_disease.py 1014B symptom_liking.py 2KB
README.txt 641B aligned_symptoms_extracting.py 8KB extract_symptom.py 3KB top_disease.py 4KB label __init__.py 0B preprocess_label.py 7KB get_slot_from_goal.py 2KB frequency.py 6KB statistics.py 5KB run_pre.py 4KB src __init__.py 0B dqn_gym.py 2KB classifier __init__.py 0B data stopwords.txt 20KB self_report_as_feature __init__.py 32B report_classifier.py 7KB __pycache__ corpus.cpython-36.pyc 3KB find_dataset.py 14KB symptom_as_feature __init__.py 33B symptom_classifier.py 14KB run __init__.py 0B run_cla.py 3KB dialogue_system agent __init__.py 139B agent.py 14KB agent_actor_critic_2.py 2KB agent_dqn.py 3KB agent_random.py 854B agent_rule.py 5KB agent_actor_critic.py 2KB __init__.py 0B dialogue_configuration.py 1KB state_tracker __init__.py 28B state_tracker.py 9KB __pycache__ __init__.cpython-36.pyc 225B dialogue_manager __init__.py 31B __pycache__ __init__.cpython-36.pyc 231B dialogue_manager.py 7KB policy_learning __init__.py 46B actor_critic_4.py 9KB actor_critic_2.py 5KB actor_critic_3.py 5KB dqn.py 24KB actor_critic.py 11KB gym_actor_critic.py 4KB tf_gradient.py 4KB utilities __init__.py 0B slot_distribution.py 4KB draw_learning_curve.py 6KB goal_to_slot.py 724B goal_action_slots_dumper.py 5KB user_simulator __init__.py 44B user_rule.py 513B user.py 28KB __pycache__ __init__.cpython-36.pyc 238B user_rule.cpython-36.pyc 917B model dqn learning_rate03 learning_rate_d4_e99_agent1_dqn1.p 5KB learning_rate learning_rate_d10_e999_agent1_dqn0.p 42KB learning_rate_d10_e999_agent1_dqn1.p 42KB learning_rate_d4_e499_agent1_dqn1.p 21KB learning_rate_d7_e999_agent1_dqn0.p 42KB learning_rate_d7_e999_agent1_dqn1.p 42KB
learning_rate_d7_e999_agent1_dqn0.p.png 201KB learning_rate_d4_e999_agent1_dqn1.p.png 250KB learning_rate_d4_e499_agent1_dqn1.p.png 260KB learning_rate_d4_e999_agent1_dqn1.p 53KB run running_steward.py 14KB __init__.py 30B run_for_parameter.py 14KB run.py 12KB __pycache__ __init__.cpython-36.pyc 217B __pycache__ __init__.cpython-36.pyc 181B __pycache__ __init__.cpython-36.pyc 165B branch_info.txt 184B requirements.txt 43B
acl2018-mds.zip 64KB README.md 1KB资源评论
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