Development of Dialog System within Rasa in the Movie Domain

This is the final project of the Language Understanding System course, the aim is to develop o Dialog System within Rasa in the Movie Domain capable of understanding what the user requires and of providing the correct answer.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

To succesfully run the project you need to download two external tool:

• Rasa-Core, see RasaCore

• Rasa-NLU, see RasaNLU

• Rasa additional dependecies:

  • Spacy, see Spacy
  • CRF Suite, through pip

  pip install sklearn_crfsuite
  

Make the project run

To run the system, the procedure is very straight:

• From terminal, move to the /scripts folder

cd scripts

• Choose which options you want to perform between training the NLU, training the dialogue manager and run and launch the script with the respective parameter

python bot.py <parameter>

with from train-nlu, train-dialogue, run

• The python script will perform all requested action

Deployment

1. train-nlu
   First action to be performed. Main operations are:

   • Load the training data
   • Pass the domain file (.yml) to the Trainer
   • Train the NLU
   • Results are stored in:

   /models/default/chat
   

2. train-dialogue To be performed immediately after the training of the NLU, main operations are:

   • Create the RasaNLUInterpreter with the result of the previous training as input
   • Create the Agent and setting its caracteristics: Policies (Memoization, Keras), Interpreter, configuration file of the NLU
   • Train the Agent with the stories, namely a file of exaples of conversations
   • (Optional) Enable the "Training online", that allows you to manually correct bot intents/actions to improve its precision
   • Results are stored in:

   /models/dialogue
   

3. run

   • Load the interpreter
   • Pass the interpreter to the agent
   • Run the bot

Authors

• Andrea Montagner - ID:189514