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.

研究表明，利用在不同大型细粒度QA数据集上训练的模型进行迁移学习，可以显著提高问答任务的性能表现。我们通过基本的迁移学习技术从SQuAD开始，在WikiQA和SemEval-2016（3A任务）中取得了最先进的表现。我们的模型在WikiQA中的表现比之前最好的模型高出8%以上。通过定量结果和视觉分析，我们证明精细的监督提供了比粗略监督更好的引导来学习词汇和句法信息。我们还展示了类似的迁移学习过程在蕴含任务上取得了最先进的成果。

利用大规模细粒度监督数据的迁移学习进行问答