Question Answering in NLP - Extractive and Abstractive QA with Vector Search

Learn to build question-answering systems in natural language processing through hands-on implementation of both extractive and abstractive QA approaches. Master the fundamentals of question-answering by exploring extractive QA techniques that identify and extract answers directly from text, and abstractive QA methods that generate new responses. Dive into Long Form Question Answering (LFQA) using the Haystack framework to handle complex, detailed queries requiring comprehensive responses. Build practical Q&A AI systems in Python by implementing open-domain question-answering solutions that can handle questions across various topics and domains. Develop specialized Q&A reader models in Python designed for open-domain scenarios, gaining experience with the core components needed to process questions and retrieve accurate answers from large text collections.