Title: Question Answering through Transfer Learning from Large Fine-grained Supervision Data,
Abstract: We show that the task of question answering (QA) can significantly benefit
from the transfer learning of models trained on a different large, fine-grained
QA dataset. We achieve the state of the art in two well-studied QA datasets,
WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique
from SQuAD. For WikiQA, our model outperforms the previous best model by more
than 8%. We demonstrate that finer supervision provides better guidance for
learning lexical and syntactic information than coarser supervision, through
quantitative results and visual analysis. We also show that a similar transfer
learning procedure achieves the state of the art on an entailment task.