Argicultural Literature Reading Comprehension based on Question Generation


With the maturity of deep learning technology, reading comprehension model (given an article and a question, the AI model automatically finds the answer to the question from the article) has become a key element in natural language applications. Such as knowledge extraction and knowledge graph construction can be solved through reading comprehension model. In this project, we investigate the employment of the reading comprehension model to build an agricultural knowledge graph from Taiwan agricultural literatures. One challenge, however, is that existing reading comprehension models are not tailored for agricultural literature and therefore cannot be used directly. In this project, we leverage the question generation technology as a mechanism for agricultural data augmentation, and then train the literature reading comprehension model in the agricultural field.


Keywords : Reading Comprehension Model Question Generation Argicultural Knowledge Graph
Research Project : Smart Sustainable New Agriculture Research Center (II)
Principal Investigator :
Co-Principal Investigator : Jiunn-Lin Wu