Building a Language Understanding and Question-Answering System with BERT

This project leverages BERT for creating a robust question-answering system fine-tuned on the SQuAD dataset. The system aims to improve accuracy and efficiency in understanding and responding to queries.

Table of Contents

• Introduction
• Problem Statement
• Methodology
  • Workflow Diagram
  • Architecture
• Dataset
  • Data Preparation
• Implementation
  • Algorithm/Pseudocode
  • Libraries Used
  • Integration of NLP Techniques
• Results
  • Performance
  • Interesting Results
• Project Management
  • Completed Work
  • Issues Faced
• References

Introduction

Natural Language Processing (NLP) is ever-evolving, with question-answering (QA) systems being a crucial part of various services. This project focuses on enhancing QA systems using transformer-based models like BERT, fine-tuned with the SQuAD dataset.

Problem Statement

Despite advancements in NLP, current QA systems face challenges in contextual understanding, handling ambiguous queries, and domain adaptability. This project aims to address these issues using BERT.

Methodology

Workflow Diagram

Architecture

Dataset

The dataset used is the Stanford Question Answering Dataset (SQuAD), containing over 100,000 question-answer pairs derived from more than 500 Wikipedia articles.

Data Preparation

• Tokenization
• Cleaning
• Normalization
• Answer Mapping
• Truncation & Padding
• Attention Masking

Implementation

Algorithm/Pseudocode

Detailed pseudocode for loading the model, preprocessing data, fine-tuning, and inference.

Libraries Used

• Preprocessing: string, re, defaultdict, collections
• Visualizations: Seaborn, Matplotlib, WordCloud, IPython.display
• Model and Evaluation: Transformers, Torch, BertTokenizer, BertForQuestionAnswering

Integration of NLP Techniques

Combining BERT's tokenization, attention mechanisms, and transfer learning to improve QA performance.

Steps to use

1. Select a topic you need information about:

2. Input a question can be in any human written form with typos too:

3. Finally, retrieves the Answer and Context along with it:

Results

Performance

• Exact Match (EM): 23.1%
• F1 Score: 34.07%

Interesting Results

Examples of the model's ability to handle rephrased questions effectively.

Project Management

Completed Work

• BERT Model Integration
• Data Pre-processing Pipeline
• Model Training and Evaluation
• User Interface Development

Issues Faced

• Data Quality
• Model Overfitting
• Computational Resources
• User Experience

References

1. Adversarial QA
2. Large QA Datasets
3. Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. North American Chapter of the Association for Computational Linguistics.
4. Radford, A., & Narasimhan, K. (2018). Improving Language Understanding by Generative Pre-Training.
5. Peters, M.E., Ruder, S., & Smith, N.A. (2019). To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks. ArXiv, abs/1903.05987.
6. Wynter, A.D., & Perry, D.J. (2020). Optimal Subarchitecture Extraction For BERT. ArXiv, abs/2010.10499.
7. Wang, W., Bi, B., Yan, M., Wu, C., Bao, Z., Peng, L., & Si, L. (2019). StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding. ArXiv, abs/1908.04577.
8. Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. Conference on Empirical Methods in Natural Language Processing.
9. Liu, X., Cheng, H., He, P., Chen, W., Wang, Y., Poon, H., & Gao, J. (2020). Adversarial Training for Large Neural Language Models. ArXiv, abs/2004.08994.