Design and Build a Chatbot from Scratch: Intent Classification, Entity Extraction, and Dialogue Context

Learn how to design and build a Chatbot from scratch, covering intent classification, entity extraction, and dialogue context. Explore the challenges of building audio-based systems.

00:00:04 Design and build a chatbot from scratch in two parts: design and building. Covers basics of chatbot design and machine learning concepts. Uses a bank chatbot as a running example.

The video is a live session on designing and building a chatbot from scratch.

The session focuses on the design aspects of a chatbot, including module breakdown and model choices.

The video mentions different approaches to building chatbots, including using cloud-based systems, libraries like Rasa, or building a custom system.

00:18:13 Design and build a Chatbot from Scratch: Learn about intent classification, entity extraction, and maintaining dialogue context in a chatbot system.

βœ… To design and build a functional chatbot, it is important to define the scope and list all the major scenarios or intents that the chatbot should handle.

πŸ”Ž The chatbot should have access to relevant data sets, such as FAQs, databases, text documents, and historical chat data, to provide accurate and useful responses to user queries.

🧩 The chatbot's functionality can be divided into modules, including intent classification, entity extraction, context tracking, actions, and answer generation.

00:36:21 Learn how to build an intent classification model for chatbots with few training samples per class using few-shot learning techniques like siamese networks or fine-tuning pre-trained transformer models.

πŸ€– Design choices for a chatbot include hierarchical intents to manage a large number of intents.

🧠 Building an intent classification model for a chatbot with limited data points per class requires few-shot learning.

πŸ”’ Using siamese networks or transformer models like BERT can be effective for few-shot learning in chatbot training.

00:54:29 Learn how to design and build a Chatbot from scratch by using transformers like BERT or GPT. Understand the importance of intent classification and entity extraction in the process.

πŸ’‘ Models like LSTM and BERT are effective for natural language processing tasks, including chatbot design and entity extraction.

πŸ”’ Understanding intent classification is crucial for building successful chatbots.

πŸ€– BERT-based models, like GPT and Transformers, are commonly used for chatbot design due to their ability to pre-train on large amounts of text.

01:12:36 Learn how to design and build a chatbot from scratch, including data sets, domain-specific entities, and rule-based systems. Understand the importance of proper data collection and handling uncertainty in intent classification and entity extraction. Explore the use of choice intents and manual intervention for uncertain cases.

πŸ’‘ There are various data sets available for Named Entity Recognition (NER), including domain-specific ones like health data in Hindi.

πŸ”§ To build a NER system from scratch, it is important to train the model using existing publicly available data sets and domain-specific entities.

πŸ€– Actions play a crucial role in a chatbot's functionality, and they can be defined based on the intent and entity of the user's query.

01:30:42 Designing a chatbot involves understanding dialogue context, handling context switches, and implementing question answering systems using techniques like regular expressions and semantic search. Building a chatbot for audio requires designing for voice recognition and synthesis.

πŸ€– Keeping track of dialogue context is essential for a chatbot to understand and respond accurately to user queries.

πŸ”„ There are different approaches to handling dialogue context, ranging from simple sequence tracking to more complex classification models.

πŸ” For non-transactional queries, like finding information in large text corpora, methods like regex, indexing, or semantic search can be used.

πŸŽ“ Designing a chatbot requires a good understanding of NLP, deep learning, and software engineering principles.

πŸŽ™οΈ For voice-based chatbots, the design should be modified to handle audio input and provide spoken responses.

01:48:49 Learn about the challenges in building audio-based systems like chatbots, including speech-to-text and text-to-speech conversion. Discover how to integrate chatbots with messaging platforms like Slack and WhatsApp using APIs.

🎯 Designing and building a chatbot involves speech-to-text and text-to-speech modules, with speech-to-text being a challenging aspect due to accent and other factors.

πŸ“ž Integrating the chatbot with platforms like Slack or WhatsApp can be done using their respective APIs, allowing for message sending and retrieving.

πŸ€” Knowing when to use which machine learning strategy depends on understanding the concept, limitations of models, and why specific models were created.

Summary of a video "LIVE: Design and build a Chatbot from Scratch" by Applied AI Course on YouTube.

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