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

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.

✅ 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.

🔎 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.

🤖 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.

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

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

🔒 Understanding intent classification is crucial for building successful chatbots.

🤖 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.

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

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

🤖 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.

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

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

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

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

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

🎯 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.

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

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