Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

In closed-domain Question Answering (QA), the goal is to retrieve answers questions within a specific domain. The main challenge of QA develop model that only requires small datasets for training since large-scale corpora may not be available. One approach flexible can adapt different closed domains and train on their corpora. this paper, we present novel versatile reading comprehension style (called CA-AcdQA). based pre-trained contextualized language models, Convolutional Neural Network (CNN), self-attention mechanism. captures relevance between question context sentences at levels granularity by exploring dependencies features extracted CNN. Moreover, include candidate answer identification expansion techniques reduction rewriting ambiguous questions. tuned with dataset sentence-level QA. tested four publicly-available datasets: Tesla (person), California (region), EU-law (system), COVID-QA (biomedical) against nine other approaches. Results show ALBERT variant outperforms all approaches significant increase in Exact Match F1 score. Furthermore, Covid-19 which text complicated specialized, improved considerably additional biomedical resources (an 15.9 over next highest baseline).