Intention Classifier is a module that analyzes the intention of the user’s utterance. This module modifies and combines “bi-RNN” and “Attention mechanism” to implement an Intention classification model.

• 2.1 Maintainer status: maintained
• 2.2 Maintainer: Yuri Kim, [email protected]
• 2.3 Author: Yuri Kim, [email protected]
• 2.4 License (optional):
• 2.5 Source git: https://github.com/DeepTaskHY/DM_Intent

To analyze the intention of the user's utterance, this module consists of two parts: 1)keyword extraction, 2)intention analysis. To extract the keywords of the user's utterance, we used Google Dialogflow. This module combines “bi-RNN” and “Attention mechanism” to implement an Intention classification model.

None

ros-melodic

$ sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
$ sudo apt-key adv --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654
$ sudo apt-get update  
$ sudo apt-get install ros-melodic-desktop-full  
$ sudo rosdep init  
$ rosdep update  

# or download dockerimage
$ docker pull ribin7bok/deeptask
$ docker run -e NVIDIA_VISIBLE_DEVICES=0 --volume ~/$workspace_name:/workspace -it --name social_intent ribin7bok/deeptask

requirements

$ mkdir $dir_name
$ cd $dir_name
$ mkdir src
$ catkin_make
$ cd src
$ git clone --recursive https://github.com/DeepTaskHY/DM_Intent.git
$ sudo apt-get update && sudo apt-get install python3-pip 
$ sudo apt-get install default-jdk
$ sudo apt-get install default-jre
$ sudo apt-get install python3-pyyaml

$ cd dm_intent
$ sudo pip3 install -r requirements.txt  

download files

$ cd dm_intent  
$ sh model_download.sh  

$ cd $dir_name
$ source devel/setup.bash  
$ roslaunch src/DM_Intent/dm_intent/launch/dm_intent.launch

{  
   "header": {
        "source": "perception",
        "target": ["dialog", "planning"],
        "timestamp": "1563980552.933543682",
        "content": ["human_speech"]
   },
   "human_speech":{ 
      "speech":"안녕하세요",
      "name":"이병현",
   }
}

○ header (header/recognitionResult): contain information about published time, publisher name, receiver name and content.

• timestamp: published time
• source: publish module name
• target: receive module name
• content: role of this ROS topic name

○ dialog_intent (dialog_intent/recognitionResult): contain human speech and user name.

• speech: human speech
• name: user name
• information: keyword to use for intention classification

{
    "header": {
        "target": ["planning"], 
        "content": ["dialog_intent"], 
        "timestamp": "1563980561.940629720", 
        "source": "dialog"
    }, 
    "dialog_intent": {
        "speech": "좋아진 것 같아.", 
        "intent": "단순 정보 전달", 
        "name": "이병현",
        "information": {}
    }
}

○ header (header/dialog_intent): contain information about published time, publisher name, receiver name and content.

• timestamp: published time
• source: publish module name
• target: receive module name
• content: role of this ROS topic name

○ dialog_intent (dialog_intent/dialog_intent): contain intent, human speech, name and information.

• intent: intention of the human speech
• speech: human speech
• name: user name

There are one category of parameters that can be used to configure the module: deep learning model.

8.1 model parameters

• ~data_path (string, default: None): The path where data(pickle file) is stored.
• ~RNN_SIZE (int, default: 192): rnn size
• ~EMBEDDING_SIZE (int, default: 200): embedding vector dimension
• ~ATTENTION_SIZE (int, default: 50): attention dimension
• ~L2_LEG_LAMBDA (float, default: 3.0): l2 leg lambda
• ~EPOCH (int, default: 50): epoch size
• ~BATCH_SIZE (int, default: 64): batch size
• ~N_Label (int, default: 7): label size
• ~DROPOUT_KEEP_PROB (float, default: 0.5): dropout
• ~BATCH_SIZE (int, default: 64): batch size
• ~N_Label (int, default: 7): label size
• ~DROPOUT_KEEP_PROB (float, default: 0.5): dropout

None

• Wang, Y., Huang, M., & Zhao, L. Attentionbased lstm for aspect-level sentiment classification.In Proceedings of the 2016 conference on empiricalmethods in natural language processing. 606-615, 2016
• Mikolov, T., Chen, K., Corrado, G., & Dean, J. Efficient estimation of word representations in vector space. arXiv:1301.3781. Retrieved from https://arxiv.org/abs/1301.3781 , 2013
• Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. Attention-based bidirectional long shortterm memory networks for relation classification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2. 207-212, 2016
• Jeongmin Yoon and Youngjoong Ko. Speech-Act Analysis System Based on Dialogue Level RNNCNN Effective on the Exposure Bias Problem. Journal of KIISE, 45, 9 (2018), 911-917, 2018