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Effective Analysis of Chatbot Frameworks: RASA and Dialogflow

EasyChair Preprint no. 8338

14 pagesDate: June 21, 2022


In recent times, use of AI based chatbots have increased tremendously. Chatbots have turned to be very helpful in the field of education, marketing, environmental etc. In this study the focus is maintained towards creating a chatbot for the educational organisation namely, Central University of Punjab, Bathinda. The chatbot is created with Rasa and Dialogflow. The queries and response were self-created for dataset. DIET classifier was used for Rasa for intent classification and entity extraction. BERT and RoBERTa for Rasa configuration and LSTM for predicting actions are used .Similarly, same dataset is used for creation of Dialogflow chatbot .Lastly, an analysis is performed on both to check the efficiency. This research depicts the path to create chatbots with Rasa and Dialogflow and also the effectiveness amongst them.

Keyphrases: BERT, Chatbot, conversational agent, Dialogflow, Diet, RASA

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Shalini Singh and Satwinder Singh},
  title = {Effective Analysis of Chatbot Frameworks: RASA and Dialogflow},
  howpublished = {EasyChair Preprint no. 8338},

  year = {EasyChair, 2022}}
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