Question Answering through Transfer Learning from Large Fine-grained Supervision Data

Understand

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.