| Title |
AskAI: BERT Based Contextual Enhancements Framework for Extractive Question Answering |
| Authors |
(Prashant Upadhyay) ; (Tuhina Panda) ; (Preeti Jaidka) ; (Nidhi Gupta) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.4.520 |
| Keywords |
BERT; Transfer learning; Extractive question answering; Natural language processing |
| Abstract |
This paper presents AskAI, a unique approach to extractive product question answering from the product descriptions facilitated by a chatbot, designed specifically for E-commerce websites. AskAI employs a Chrome extension and a question answering model trained on a blend of SQuAD 2.0 (Stanford Question Answering Dataset v2.0) and ePQA (eCommerce Product Question Answering) datasets. The user inputs their query, and the system automatically scrapes relevant context from the webpage. Our model leverages transfer learning, utilizing a pretrained BERT model fine-tuned initially on the SQuAD 2.0 dataset and further trained on the ePQA dataset. This approach enables the model to effectively understand and respond to user questions. By integrating contextual information directly from the webpage, AskAI delivers natural-sounding and relevant answers. The results showcase the effectiveness of our approach, including an F1 score of 78.76 on SQuAD 2.0 and 72.31 on ePQA with an Exact match of 74.82 and 68.98 on SQuAD 2.0 and ePQA datasets respectively. Thus, demonstrating the capability of the model to accurately comprehend user inquiries and provide meaningful responses. |