Question Answering System Project Guide

Traditional search systems return entire documents, but users often just need specific answers to their questions. Extractive question answering (QA) addresses this by reading a passage and extracting the exact span of text answering the query. Building an intelligent QA system using BERT leverages its deep contextual understanding, making machines capable of answering complex questions accurately — critical for education, customer support, and search engines.

BERT (Bidirectional Encoder Representations from Transformers) models can be fine-tuned on datasets like SQuAD to predict answer spans within given contexts. They excel at understanding nuances, multiple meanings, and complex sentence structures. Using BERT for QA allows building systems where users input a context and a question, and the model highlights or extracts the precise answer — offering real-world applications in e-learning, customer query bots, legal research, and healthcare assistance.

The system accepts a passage and a question as input. BERT processes both simultaneously to predict the start and end positions of the answer span in the passage. Fine-tuning BERT on large QA datasets like SQuAD or Natural Questions adapts the model for diverse domains. Post-processing ensures extracted answers are grammatically coherent and contextually complete, enabling accurate and reliable answers for user queries.

• Collect datasets like SQuAD 1.1/2.0, TriviaQA, or Natural Questions containing (context, question, answer) triples.
• Preprocess: tokenize using WordPiece tokenizers, manage maximum sequence lengths, and map answers to token positions.
• Fine-tune pre-trained BERT models (e.g., `bert-base-uncased`) for extractive QA tasks.
• Evaluate using Exact Match (EM) score and F1-score for predicting correct answer spans accurately.
• Deploy the QA system into a web or mobile app, where users input passages and questions to retrieve instant answers.

• SQuAD Dataset for QA
• Hugging Face Transformers QA Pipelines
• Fine-Tuning BERT for Question Answering