Project Background:
Machine reading comprehension (MRC) centers on developing artificial intelligence (AI) systems capable of understanding and answering questions posed in natural language based on given texts or documents. This task presents significant challenges due to the complexity of human language understanding and the diverse range of questions that may be asked. Traditional approaches to MRC often relied on handcrafted features or shallow learning models, which limited their ability to comprehend language structures and context-dependent information. The recent advancements in deep learning in natural language processing (NLP) have revolutionized MRC by enabling the automatic extraction of intricate linguistic patterns and representations from large corpora of text data. Deep learning models such as transformer-based architectures, exemplified by BERT, have shown remarkable performance in MRC tasks by leveraging pretraining on massive text corpora and fine-tuning MRC datasets. This project work aims to further advance the MRC by exploring novel architectures, techniques, and datasets to improve the accuracy, efficiency, and generalization capabilities of machine reading comprehension systems.
Problem Statement
- Traditional systems struggle to comprehend the nuances of human language, hindering their ability to answer questions based on given texts or documents accurately.
- Requires models to understand the context of a passage and interpret it accurately to provide relevant answers to questions posed in natural language.
-
MRC systems must be able to handle various types of questions, including fact-based, reasoning-based, and inference-based questions, each requiring different levels of understanding and reasoning.
- Natural language is inherently ambiguous and nuanced, making it challenging for machines to accurately interpret and answer questions, especially when faced with vague or ambiguous text passages.
-
Need to scale effectively to handle large datasets and generalize well to unseen data and diverse domains, ensuring their applicability in real-world scenarios.
Aim and Objectives
- To develop advanced machine learning models capable of understanding natural language passages and accurately answering questions posed in various formats.
- Improve the accuracy and efficiency of machine reading comprehension models through novel architectures and techniques.
- Enhance the models ability to comprehend complex language structures and context-dependent information.
- Address diverse question types with appropriate reasoning mechanisms, including fact-based, reasoning-based, and inference-based questions.
- Investigate techniques for handling ambiguity and uncertainty in natural language passages to improve answer quality.
- Validate the performance of the developed models through rigorous evaluation of benchmark datasets and real-world applications.
Contributions to Machine Reading Comprehension
- Developed state-of-the-art machine learning models capable of accurately understanding and answering questions in natural language.
- Enhanced the accuracy and efficiency of MRC models through innovative architectures and techniques, leading to better performance on benchmark datasets.
- Addressed diverse question types, including fact-based, reasoning-based, and inference-based questions, with specialized reasoning mechanisms, improving the model versatility and applicability.
- Investigated techniques to handle ambiguity and uncertainty in natural language passages, improving the robustness and reliability of answer generation.
- Validated the effectiveness of the developed models through rigorous evaluation of benchmark datasets and real-world applications, demonstrating their utility and performance improvements over existing methods.
Deep Learning Algorithms for Machine Reading Comprehension
- BERT (Bidirectional Encoder Representations from Transformers)
-
RoBERTa (Robustly Optimized BERT Approach)
-
GPT (Generative Pre-trained Transformer)
-
GPT-2
-
GPT-3
-
Transformer-XL
-
XLNet
-
ALBERT (A Lite BERT)
-
DistilBERT
-
ELECTRA
Datasets for Machine Reading Comprehension
- SQuAD (Stanford Question Answering Dataset)
-
SQuAD 2.0
-
NewsQA
-
CoQA (Conversational Question Answering)
-
RACE (The ReAding Comprehension from Examinations)
-
MRC (Machine Reading Comprehension)
-
NarrativeQA
-
MCScript
-
Cosmos QA
-
HotpotQA
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
