Designing and Implementing Conversational Intelligent Chat-bot Using Natural Language Processing
| Autor: | Rupamita Sarkar, Swastik Mitra, Asoke Nath, Rohitaswa Pradhan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Intelligent agent
Human–computer interaction Computer science 0202 electrical engineering electronic engineering information engineering Natural language understanding Natural language generation Deep neural networks 020206 networking & telecommunications 020201 artificial intelligence & image processing 02 engineering and technology computer.software_genre computer Chatbot |
| Zdroj: | International Journal of Scientific Research in Computer Science, Engineering and Information Technology. :262-266 |
| ISSN: | 2456-3307 |
| DOI: | 10.32628/cseit217351 |
| Popis: | In the early days of Artificial Intelligence, it was observed that tasks which humans consider ‘natural’ and ‘commonplace’, such as Natural Language Understanding, Natural Language Generation and Vision were the most difficult task to carry over to computers. Nevertheless, attempts to crack the proverbial NLP nut were made, initially with methods that fall under ‘Symbolic NLP’. One of the products of this era was ELIZA. At present the most promising forays into the world of NLP are provided by ‘Neural NLP’, which uses Representation Learning and Deep Neural networks to model, understand and generate natural language. In the present paper the authors tried to develop a Conversational Intelligent Chatbot, a program that can chat with a user about any conceivable topic, without having domain-specific knowledge programmed into it. This is a challenging task, as it involves both ‘Natural Language Understanding’ (the task of converting natural language user input into representations that a machine can understand) and subsequently ‘Natural Language Generation’ (the task of generating an appropriate response to the user input in natural language). Several approaches exist for building conversational chatbots. In the present paper, two models have been used and their performance has been compared and contrasted. The first model is purely generative and uses a Transformer-based architecture. The second model is retrieval-based, and uses Deep Neural Networks. |
| Databáze: | OpenAIRE |
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