Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network (SynNet). Given a high-performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed SynNet with a pretrained model from the SQuAD dataset on the challenging NewsQA dataset, we achieve an F1 measure of 44.3 model and 46.6 (F1 measure of 50.0 without use of provided annotations.
READ FULL TEXT