This paper explores the fine-tuning of pretrained language models for the task of Question Answering (QA) within a specific domain. The approach focuses on leveraging the power of state-of-the-art models such as BERT, ALBERT, RoBERTa, MobileBERT, TinyBERT, and ELECTRA to address domain-specific QA challenges. Using a CoQA-like dataset, we investigate the effectiveness of these models when fine-tuned on a question-answering task, demonstrating improvements in accuracy and performance. Our experiments reveal key insights into the behavior of these models and their ability to adapt to a specialized domain, using a combination of evaluation metrics such as Exact Match (EM) and F1 scores. Additionally, we present visualizations of model performance, token distributions, and frequent term analysis to better understand their efficiency. The results show that fine-tuning these models yields significant improvements over base models, confirming their applicability in real-world domain-specific QA tasks.
CoQA (Conversational Question Answering) is a benchmark designed to evaluate a model's ability to understand multi-turn, contextual conversations, rather than just isolated Q&A. Models like MobileBERT, TinyBERT, RoBERTa, and ELECTRA were introduced to either optimize BERT for edge devices (e.g., mobile apps) or improve training efficiency and performance. Benchmarking them on CoQA provides insights into how well these compact or optimized models handle conversational context, a critical aspect of real-world dialogue systems. This evaluation helps us understand their performance in related tasks such as
- Semantic re-ranking (e.g., choosing the best response)
- Context-aware encoding for dialogue systems
- Memory-driven chatbots -Named Entity Recognition (NER) in multi-turn contexts, -And other downstream NLP tasks requiring contextual understanding.