Designing effective chatbot system using gru with beam search
By: Thorat, Sandeep A
.
Contributor(s): Jadhav, Vishakha D.
Publisher: Chennai ICT Academy 2022Edition: Vol.13(1), Oct.Description: 2750-2755p.Subject(s): Computer EngineeringOnline resources: Click here
In:
ICTACT Journal on Soft Computing (IJSC)Summary:
Artificial Intelligence (AI) based Chatbot is a moderately new
technology in the world. AI and Natural Language Processing (NLP)
empowers a Chatbot to converse like a human being. Chatbots have
become popular recently as they diminish human efforts by automating
various tasks. AI-based Chatbot learns from the previous discussion
and generates an appropriate response or action for the input given by
the user. In the proposed research work we designed AI-based Chatbot
system using the Sequence to Sequence (Seq2Seq) model. This system
uses a Gated Recurrent Unit (GRU) for encoder and decoder. In the
proposed model the GRU encoder accepts a query from the user. The
GRU encoder uses an attention mechanism to consider only relevant
information and convert it into the context vector form. A context vector
is another input to the GRU decoder. The GRU decoder generates a
response using the Beam search algorithm. The research work uses
Cornell Movie Dialogue Corpus to train the proposed interactive
Chatbot system. It is observed that the proposed model with the
combination of GRU and Beam search gives better accuracy with the
minimum loss for testing data. These experimental results are better
than existing approaches that use LSTM Seq2Seq models to train
Chatbot systems.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-0511 |
Artificial Intelligence (AI) based Chatbot is a moderately new
technology in the world. AI and Natural Language Processing (NLP)
empowers a Chatbot to converse like a human being. Chatbots have
become popular recently as they diminish human efforts by automating
various tasks. AI-based Chatbot learns from the previous discussion
and generates an appropriate response or action for the input given by
the user. In the proposed research work we designed AI-based Chatbot
system using the Sequence to Sequence (Seq2Seq) model. This system
uses a Gated Recurrent Unit (GRU) for encoder and decoder. In the
proposed model the GRU encoder accepts a query from the user. The
GRU encoder uses an attention mechanism to consider only relevant
information and convert it into the context vector form. A context vector
is another input to the GRU decoder. The GRU decoder generates a
response using the Beam search algorithm. The research work uses
Cornell Movie Dialogue Corpus to train the proposed interactive
Chatbot system. It is observed that the proposed model with the
combination of GRU and Beam search gives better accuracy with the
minimum loss for testing data. These experimental results are better
than existing approaches that use LSTM Seq2Seq models to train
Chatbot systems.
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